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@inproceedings{vrunda2024,
title={Children’s Speech Recognition through Discrete Token Enhancement},
author={Sukhadia, Vrunda N. and Chowdhury, Shammur Absar },
booktitle = {{Proc. of the 25th Annual Conference of the International Speech Communication Association (INTERSPEECH)}},
year={2024}
}
@inproceedings{ElKheir2024Beyond,
title={Beyond Orthography: Automatic Recovery of Short Vowels and Dialectal Sounds in Arabic},
author={Yassine El Kheir and Hamdy Mubarak and Ahmed Ali and Shammur Absar Chowdhury},
booktitle={Proc. of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL)},
year={2024}
}
@inproceedings{el2024l1,
title={L1-aware multilingual mispronunciation detection framework},
author={El Kheir, Yassine and Chowdhury, Shammur Absar and Ali, Ahmed},
booktitle={Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={12752--12756},
year={2024},
organization={IEEE}
}
@inproceedings{el2024speech,
title={Speech representation analysis based on inter-and intra-model similarities},
author={El Kheir, Yassine and Ali, Ahmed and Chowdhury, Shammur Absar},
booktitle={2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)},
pages={848--852},
year={2024},
organization={IEEE}
}
@inproceedings{hussein2024speech,
title={Speech collage: code-switched audio generation by collaging monolingual corpora},
author={Hussein, Amir and Zeinali, Dorsa and Klejch, Ond{\v{r}}ej and Wiesner, Matthew and Yan, Brian and Chowdhury, Shammur and Ali, Ahmed and Watanabe, Shinji and Khudanpur, Sanjeev},
booktitle={Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={12006--12010},
year={2024},
organization={IEEE}
}
@inproceedings{ElKheir2024,
author = {Yassine El Kheir and Ahmed Ali and Shammur Absar Chowdhury},
title = {Speech Representation Analysis Based on Inter- and Intra-Model Similarities},
booktitle = {Proc. of the Explainable Machine Learning for Speech and Audio Workshop, ICASSP},
year = {2024},
}
@inproceedings{kheir2023automatic,
title={Automatic Pronunciation Assessment-A Review},
author={Kheir, Yassine and Ali, Ahmed and Chowdhury, Shammur},
booktitle={Proc. of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
pages={8304--8324},
year={2023}
}
@inproceedings{speechBlender2023,
author = {Yassine El Kheir and Shammur Absar Chowdhury and Ahmed Ali},
title = {Speechblender: Speech augmentation framework for mispronunciation data generation},
booktitle = {Proc. of the Speech and Language Technology in Education (SLaTE)},
year = {2023}
}
@inproceedings{multiview2023,
author = {Yassine El Kheir and Shammur Absar Chowdhury and Ahmed Ali},
title = {Multi-View Multi-Task Representation Learning for Mispronunciation Detection},
booktitle = {Proc. of the Speech and Language Technology in Education (SLaTE)},
year = {2023}
}
@article{chowdhury2023end,
title={What do end-to-end speech models learn about speaker, language and channel information? a layer-wise and neuron-level analysis},
author={Chowdhury, Shammur Absar and Durrani, Nadir and Ali, Ahmed},
journal={Computer Speech \& Language},
volume={83},
pages={101539},
year={2023},
publisher={Elsevier}
}
@inproceedings{hamed2023benchmarking,
title={Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition},
author={Hamed, Injy and Hussein, Amir and Chellah, Oumnia and Chowdhury, Shammur and Mubarak, Hamdy and Sitaram, Sunayana and Habash, Nizar and Ali, Ahmed},
booktitle={Proc. of the 2022 IEEE Spoken Language Technology Workshop (SLT)},
pages={999--1005},
year={2022},
organization={IEEE}
}
@inproceedings{hussein2023textual,
title={Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition},
author={Hussein, Amir and Chowdhury, Shammur Absar and Abdelali, Ahmed and Dehak, Najim and Ali, Ahmed and Khudanpur, Sanjeev},
booktitle={Proc. of the 2022 IEEE Spoken Language Technology Workshop (SLT)},
pages={777--784},
year={2022},
organization={IEEE}
}
@inproceedings{chowdhury2023multilingual,
title={MULTILINGUAL WORD ERROR RATE ESTIMATION: E-WER3},
author={Chowdhury, Shammur Absar and Ali, Ahmed},
booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year={2023}
}
@inproceedings{kheir2023qvoice,
title={QVoice: Arabic Speech Pronunciation Learning Application},
author={Kheir, Yassine El and Khnaisser, Fouad and Chowdhury, Shammur Absar and Mubarak, Hamdy and Afzal, Shazia and Ali, Ahmed},
booktitle={INTERSPEECH},
year={2023}
}
@inproceedings{elshahawy2023myvoice,
title={MyVoice: Arabic Speech Resource Collaboration Platform},
author={Elshahawy, Yousseif and Kheir, Yassine El and Chowdhury, Shammur Absar and Ali, Ahmed},
booktitle={INTERSPEECH},
year={2023}
}
@inproceedings{hamed2022benchmarking,
title={Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition},
author={Hamed, Injy and Hussein, Amir and Chellah, Oumnia and Chowdhury, Shammur and Mubarak, Hamdy and Sitaram, Sunayana and Habash, Nizar and Ali, Ahmed},
booktitle={2022 IEEE Spoken Language Technology Workshop (SLT)},
year={2022}
}
@inproceedings{zamparelli2022semeval,
title={SemEval-2022 Task 3: PreTENS-Evaluating Neural Networks on Presuppositional Semantic Knowledge},
author={Zamparelli, Roberto and Chowdhury, Shammur and Brunato, Dominique and Chesi, Cristiano and Dell’Orletta, Felice and Hasan, Md Arid and Venturi, Giulia},
booktitle={Proc. of the 16th International Workshop on Semantic Evaluation (SemEval-2022)},
pages={228--238},
year={2022}
}
@inproceedings{bayerl2022can,
title={What can Speech and Language Tell us About the Working Alliance in Psychotherapy},
author={Bayerl, Sebastian P and Roccabruna, Gabriel and Chowdhury, Shammur Absar and Ciulli, Tommaso and Danieli, Morena and Riedhammer, Korbinian and Riccardi, Giuseppe},
booktitle = {{{{Proc. of the 23rd Annual Conference of the International Speech Communication Association (INTERSPEECH)}}}},
year={2022}
}
@article{mubarak2022emojis,
title={Emojis as anchors to detect arabic offensive language and hate speech},
author={Mubarak, Hamdy and Hassan, Sabit and Chowdhury, Shammur Absar},
journal={Natural Language Engineering (NLE) Journal},
year={2022}
}
@article{jansen2021persona,
title={Persona analytics: Analyzing the stability of online segments and content interests over time using non-negative matrix factorization},
author={Jansen, Bernard J and Jung, Soon-gyo and Chowdhury, Shammur A and Salminen, Joni},
journal={Expert Systems with Applications},
volume={185},
pages={115611},
year={2021},
publisher={Pergamon}
}
@article{ali_connecting_2021,
title = {Connecting {Arabs}: bridging the gap in dialectal speech recognition},
volume = {64},
number = {4},
journal = {Communications of the ACM},
author = {Ali, Ahmed and Chowdhury, Shammur and Afify, Mohamed and El-Hajj, Wassim and Hajj, Hazem and Abbas, Mourad and Hussein, Amir and Ghneim, Nada and Abushariah, Mohammad and Alqudah, Assal},
year = {2021},
note = {Publisher: ACM New York, NY, USA},
pages = {124--129},
}
@inproceedings{chowdhury_towards_2021,
title = {Towards {One} {Model} to {Rule} {All}: {Multilingual} {Strategy} for {Dialectal} {Code}-{Switching} {Arabic} {ASR}},
booktitle = {{{{Proc. of the 22nd Annual Conference of the International Speech Communication Association (INTERSPEECH)}}}},
author = {Chowdhury, Shammur Absar and Hussein, Amir and Abdelali, Ahmed and Ali, Ahmed},
year = {2021}
}
@inproceedings{ali_arabic_2021,
title = {Arabic {Code}-{Switching} {Speech} {Recognition} using {Monolingual} {Data}},
booktitle = {{{{Proc. of the 22nd Annual Conference of the International Speech Communication Association (INTERSPEECH)}}}},
author = {Ali, Ahmed and Chowdhury, Shammur and Hussein, Amir and Yasser, Hifny},
year = {2021},
}
@inproceedings{mubarak_qasr_2021,
title = {{QASR}: {QCRI} {Aljazeera} {Speech} {Resource}. {A} {Large} {Scale} {Annotated} {Arabic} {Speech} {Corpus}},
booktitle = {{Proc. of the 59th Annual Meeting of the Association for Computational Linguistics (ACL)}},
author = {Mubarak, Hamdy and Hussein, Amir and Chowdhury, Shammur Absar and Ali, Ahmed},
year = {2021},
}
@article{islam2024datanarrative,
title={{D}ata{N}arrative: Automated Data-Driven Storytelling with Visualizations and Texts},
author={Islam, Mohammed Saidul and Hoque, Enamul and Joty, Shafiq and Laskar, Md Tahmid Rahman and Parvez, Md Rizwan},
journal={arXiv preprint arXiv:2408.05346},
year={2024}
}
@article{laskar2024systematic,
title={A systematic survey and critical review on evaluating large language models: Challenges, limitations, and recommendations},
author={Laskar, Md Tahmid Rahman and Alqahtani, Sawsan and Bari, M Saiful and Rahman, Mizanur and Khan, Mohammad Abdullah Matin and Khan, Haidar and Jahan, Israt and Bhuiyan, Amran and Tan, Chee Wei and Parvez, Md Rizwan and others},
journal={arXiv preprint arXiv:2407.04069},
year={2024}
}
@article{islam2024open,
title={{OPEN}-{RAG}: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models},
author={Islam, Shayekh Bin and Rahman, Md Asib and Hossain, KSM and Hoque, Enamul and Joty, Shafiq and Parvez, Md Rizwan},
journal={arXiv preprint arXiv:2410.01782},
year={2024}
}
@inproceedings{masry-etal-2024-chartinstruct,
title = "{C}hart{I}nstruct: Instruction Tuning for Chart Comprehension and Reasoning",
author = "Masry, Ahmed and
Shahmohammadi, Mehrad and
Parvez, Md Rizwan and
Hoque, Enamul and
Joty, Shafiq",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.619",
doi = "10.18653/v1/2024.findings-acl.619",
pages = "10387--10409",
abstract = "Charts provide visual representations of data and are widely used for analyzing information, addressing queries, and conveying insights to others. Various chart-related downstream tasks have emerged recently, such as question-answering and summarization. A common strategy to solve these tasks is to fine-tune various models originally trained on vision tasks language. However, such task-specific models are not capable of solving a wide range of chart-related tasks, constraining their real-world applicability. To overcome these challenges, we introduce ChartInsruct: a novel chart-specific vision-language Instruction-following dataset comprising 191K instructions generated with 71K charts. We then present two distinct systems for instruction tuning on such datasets: (1) an end-to-end model that connects a vision encoder for chart understanding with a LLM; and (2) a pipeline model that employs a two-step approach to extract chart data tables and input them into the LLM. In experiments on four downstream tasks, we first show the effectiveness of our model{--}achieving a new set of state-of-the-art results. Further evaluation shows that our instruction-tuning approach supports a wide array of real-world chart comprehension and reasoning scenarios, thereby expanding the scope and applicability of our models to new kinds of tasks.",
}
@inproceedings{islam-etal-2024-mapcoder,
title = "{M}ap{C}oder: Multi-Agent Code Generation for Competitive Problem Solving",
author = "Islam, Md. Ashraful and
Ali, Mohammed Eunus and
Parvez, Md Rizwan",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.269",
doi = "10.18653/v1/2024.acl-long.269",
pages = "4912--4944",
abstract = "Code synthesis, which requires a deep understanding of complex natural language (NL) problem descriptions, generation of code instructions for complex algorithms and data structures, and the successful execution of comprehensive unit tests, presents a significant challenge. Thus, while large language models (LLMs) demonstrate impressive proficiency in natural language processing (NLP), their performance in code generation tasks remains limited. In this paper, we introduce a new approach to code generation tasks leveraging the multi-agent prompting that uniquely replicates the full cycle of program synthesis as observed in human developers. Our framework, MapCoder, consists of four LLM agents specifically designed to emulate the stages of this cycle: recalling relevant examples, planning, code generation, and debugging. After conducting thorough experiments, with multiple LLMs ablations and analyses across eight challenging competitive problem-solving and program synthesis benchmarks{---}MapCoder showcases remarkable code generation capabilities, achieving their new state-of-the-art (pass@1) results{---}(HumanEval 93.9{\%}, MBPP 83.1{\%}, APPS 22.0{\%}, CodeContests 28.5{\%}, and xCodeEval 45.3{\%}). Moreover, our method consistently delivers superior performance across various programming languages and varying problem difficulties. We open-source our framework at https://github.com/Md-Ashraful-Pramanik/MapCoder.",
}
@inproceedings{boughorbel-etal-2024-improving,
title = "Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis",
author = "Boughorbel, Sabri and
Parvez, Md Rizwan and
Hawasly, Majd",
editor = "Habash, Nizar and
Bouamor, Houda and
Eskander, Ramy and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Abdelali, Ahmed and
Touileb, Samia and
Hamed, Injy and
Onaizan, Yaser and
Alhafni, Bashar and
Antoun, Wissam and
Khalifa, Salam and
Haddad, Hatem and
Zitouni, Imed and
AlKhamissi, Badr and
Almatham, Rawan and
Mrini, Khalil",
booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.arabicnlp-1.7",
doi = "10.18653/v1/2024.arabicnlp-1.7",
pages = "73--88",
abstract = "Training LLMs in low resources languages usually utilizes machine translation (MT) data augmentation from English language. However, translation brings a number of challenges: there are large costs attached to translating and curating huge amounts of content with high-end machine translation solutions; the translated content carries over cultural biases; and if the translation is not faithful and accurate, the quality of the data degrades causing issues in the trained model. In this work, we investigate the role of translation and synthetic data in training language models. We translate TinyStories, a dataset of 2.2M short stories for 3-4 year old children, from English to Arabic using the open NLLB-3B MT model. We train a number of story generation models of size 1M-33M parameters using this data. We identify a number of quality and task-specific issues in the resulting models. To rectify these issues, we further pre-train the models with a small dataset of synthesized high-quality stories generated by a capable LLM in Arabic, representing 1{\%} of the original training data. We show, using GPT-4 as a judge and dictionary learning analysis from mechanistic interpretability, that the suggested approach is a practical means to resolve some of the translation pitfalls. We illustrate the improvement through case studies of linguistic and cultural bias issues.",
}
@inproceedings{hawasly2024scaling,
title={Scaling up Discovery of Latent Concepts in Deep NLP Models},
author={Hawasly, Majd and Dalvi, Fahim and Durrani, Nadir},
booktitle={Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={793--806},
year={2024}
}
@inproceedings{boughorbel2023analyzing,
title={Analyzing Multilingual Competency of LLMs in Multi-Turn Instruction Following: A Case Study of Arabic},
author={Boughorbel, Sabri and Hawasly, Majd},
booktitle={Proceedings of ArabicNLP 2023},
pages={128--139},
year={2023}
}
@inproceedings{khan-etal-2024-xcodeeval,
title = "{XC}ode{E}val: An Execution-based Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval",
author = "Khan, Mohammad Abdullah Matin and
Bari, M Saiful and
Long, Do and
Wang, Weishi and
Parvez, Md Rizwan and
Joty, Shafiq",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.367",
doi = "10.18653/v1/2024.acl-long.367",
pages = "6766--6805",
abstract = "Recently, pre-trained large language models (LLMs) have shown impressive abilities in generating codes from natural language descriptions, repairing buggy codes, translating codes between languages, and retrieving relevant code segments. However, the evaluation of these models has often been performed in a scattered way on only one or two specific tasks, in a few languages, at a partial granularity (e.g., function) level, and in many cases without proper training data. Even more concerning is that in most cases the evaluation of generated codes has been done in terms of mere lexical overlap with a reference code rather than actual execution. We introduce *xCodeEval*, the largest executable multilingual multitask benchmark to date consisting of 25 M document-level coding examples (16.5 B tokens) from about 7.5 K unique problems covering up to 11 programming languages with execution-level parallelism. It features a total of 7 tasks involving code understanding, generation, translation and retrieval. *xCodeEval* adopts an execution-based evaluation and offers a multilingual code execution engine, *ExecEval* that supports unit test based execution in all the 11 languages. To address the challenge of balancing the distributions of text-code samples over multiple attributes in validation/test sets, we propose a novel data splitting and a data selection schema based on the geometric mean and graph-theoretic principle. Our experiments with OpenAI{'}s LLMs (zero-shot) and open-LLMs (zero-shot and fine-tuned) on the tasks and languages demonstrate to be quite challenging as per the current advancements in language models.",
}
@article{mousi2024aradicebenchmarksdialectalcultural,
title={{AraDiCE}: Benchmarks for Dialectal and Cultural Capabilities in LLMs},
author={Basel Mousi and Nadir Durrani and Fatema Ahmad and Md. Arid Hasan and Maram Hasanain and Tameem Kabbani and Fahim Dalvi and Shammur Absar Chowdhury and Firoj Alam},
year={2024},
journal={arXiv preprint arXiv:2409.11404},
archivePrefix={arXiv},
eprint={2409.11404},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2409.11404},
}
@article{kmainasi2024llamalensspecializedmultilingualllm,
title={{LlamaLens}: Specialized Multilingual LLM for Analyzing News and Social Media Content},
author={Mohamed Bayan Kmainasi and Ali Ezzat Shahroor and Maram Hasanain and Sahinur Rahman Laskar and Naeemul Hassan and Firoj Alam},
year={2024},
journal={arXiv preprint arXiv:2410.15308},
archivePrefix={arXiv},
eprint={2410.15308},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.15308},
}
@inproceedings{kmainasi2024nativevsnonnativelanguage,
title={Native vs Non-Native Language Prompting: A Comparative Analysis},
booktitle = {Proceedings of The 25th International Web Information Systems Engineering Conference (WISE)},
year = {2024},
address = {Doha, Qatar},
url={https://arxiv.org/abs/2409.07054},
}
@inproceedings{alam2024propagandahatemultimodalanalysis,
title={Propaganda to Hate: A Multimodal Analysis of Arabic Memes with Multi-Agent LLMs},
author={Firoj Alam and Md. Rafiul Biswas and Uzair Shah and Wajdi Zaghouani and Georgios Mikros},
booktitle = {Proceedings of The 25th International Web Information Systems Engineering Conference (WISE)},
year = {2024},
address = {Doha, Qatar},
url={https://arxiv.org/abs/2409.07246},
}
@article{hasan2024nativqa,
bibtex_show={true},
title = {{NativQA}: Multilingual Culturally-Aligned Natural Query for LLMs},
author = {
Md. Arid Hasan and
Maram Hasanain and
Fatema Ahmad and
Sahinur Rahman Laskar and
Sunaya Upadhyay and
Vrunda N Sukhadia and
Mucahid Kutlu and
Shammur Absar Chowdhury and
Firoj Alam
},
year = {2024},
url={https://arxiv.org/abs/2407.09823},
publisher = {arXiv:2407.09823},
}
@inproceedings{alam2024armeme,
title={{ArMeme}: Propagandistic Content in Arabic Memes},
author={Alam, Firoj and Hasnat, Abul and Ahmed, Fatema and Hasan, Md Arid and Hasanain, Maram},
year={2024},
booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
address={Miami, Florida},
month={November 12–16},
publisher={Association for Computational Linguistics},
journal={arXiv: 2406.03916},
}
@article{ThatiAR2024,
title={{ThatiAR}: Subjectivity Detection in Arabic News Sentences},
author={Reem Suwaileh and Maram Hasanain and Fatema Hubail and Wajdi Zaghouani and Firoj Alam},
year={2024},
journal={arXiv: 2406.05559},
}
@inproceedings{hasanain-etal-2024-araieval,
title = "{A}r{AIE}val Shared Task: Propagandistic Techniques Detection in Unimodal and Multimodal {A}rabic Content",
author = "Hasanain, Maram and
Hasan, Md. Arid and
Ahmad, Fatema and
Suwaileh, Reem and
Biswas, Md. Rafiul and
Zaghouani, Wajdi and
Alam, Firoj",
editor = "Habash, Nizar and
Bouamor, Houda and
Eskander, Ramy and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Abdelali, Ahmed and
Touileb, Samia and
Hamed, Injy and
Onaizan, Yaser and
Alhafni, Bashar and
Antoun, Wissam and
Khalifa, Salam and
Haddad, Hatem and
Zitouni, Imed and
AlKhamissi, Badr and
Almatham, Rawan and
Mrini, Khalil",
booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.arabicnlp-1.44",
pages = "456--466",
abstract = "We present an overview of the second edition of the ArAIEval shared task, organized as part of the ArabicNLP 2024 conference co-located with ACL 2024. In this edition, ArAIEval offers two tasks: (i) detection of propagandistic textual spans with persuasion techniques identification in tweets and news articles, and (ii) distinguishing between propagandistic and non-propagandistic memes. A total of 14 teams participated in the final evaluation phase, with 6 and 9 teams participating in Tasks 1 and 2, respectively. Finally, 11 teams submitted system description papers. Across both tasks, we observed that fine-tuning transformer models such as AraBERT was at the core of the majority of the participating systems. We provide a description of the task setup, including a description of the dataset construction and the evaluation setup. We further provide a brief overview of the participating systems. All datasets and evaluation scripts are released to the research community. We hope this will enable further research on these important tasks in Arabic.",
}
@InProceedings{CheckThat:ECIR2024,
author="Barr{\'o}n-Cede{\~{n}}o, Alberto
and Alam, Firoj
and Chakraborty, Tanmoy
and Elsayed, Tamer
and Nakov, Preslav
and Przyby{\l}a, Piotr
and Stru{\ss}, Julia Maria
and Haouari, Fatima
and Hasanain, Maram
and Ruggeri, Federico
and Song, Xingyi
and Suwaileh, Reem",
editor="Goharian, Nazli
and Tonellotto, Nicola
and He, Yulan
and Lipani, Aldo
and McDonald, Graham
and Macdonald, Craig
and Ounis, Iadh",
title="The {CLEF}-2024 {C}heck{T}hat! {L}ab: Check-Worthiness, Subjectivity, Persuasion, Roles, Authorities, and Adversarial Robustness",
booktitle="Advances in Information Retrieval",
year="2024",
publisher="Springer Nature Switzerland",
NOaddress="Cham",
pages="449--458",
abstract="The first five editions of the CheckThat! lab focused on the main tasks of the information verification pipeline: check-worthiness, evidence retrieval and pairing, and verification. Since the 2023 edition, it has been focusing on new problems that can support the research and decision making during the verification process. In this new edition, we focus on new problems and ---for the first time--- we propose six tasks in fifteen languages (Arabic, Bulgarian, English, Dutch, French, Georgian, German, Greek, Italian, Polish, Portuguese, Russian, Slovene, Spanish, and code-mixed Hindi-English): Task 1 estimation of check-worthiness (the only task that has been present in all CheckThat! editions), Task 2 identification of subjectivity (a follow up of CheckThat! 2023 edition), Task 3 identification of persuasion (a follow up of SemEval 2023), Task 4 detection of hero, villain, and victim from memes (a follow up of CONSTRAINT 2022), Task 5 Rumor Verification using Evidence from Authorities (a first), and Task 6 robustness of credibility assessment with adversarial examples (a first). These tasks represent challenging classification and retrieval problems at the document and at the span level, including multilingual and multimodal settings.",
isbn="978-3-031-56069-9",
}
@inproceedings{sadraeijavaeri2024superpos,
title={SuperPos-Prompt: Enhancing Soft Prompt Tuning of Language Models with Superposition of Multi Token Embeddings},
author={SadraeiJavaeri, MohammadAli and Asgari, Ehsaneddin and McHardy, Alice Carolyn and Rabiee, Hamid Reza},
booktitle = "NeurIPS 2024 Workshop on Efficient Natural Language and Speech Processing",
series = {NeurIPS~'24},
month = "dec",
year = "2024",
address = "Vancouver, Canada",
}
@inproceedings{zahraei2024turingq,
title={TuringQ: Benchmarking AI Comprehension in Theory of Computation},
author={Zahraei, Pardis Sadat and Asgari, Ehsaneddin},
booktitle = "The 2024 Conference on Empirical Methods in Natural Language Processing",
series={EMNLP~'24},
month = "nov",
year = "2024",
publisher = "Association for Computational Linguistics",
}
@inproceedings{ghahroodi2024khayyam,
title={Khayyam Challenge (PersianMMLU): Is Your LLM Truly Wise to The Persian Language?},
author={Ghahroodi, Omid and Nouri, Marzia and Sanian, Mohammad Vali and Sahebi, Alireza and Dastgheib, Doratossadat and Asgari, Ehsaneddin and Baghshah, Mahdieh Soleymani and Rohban, Mohammad Hossein},
booktitle = "Proceedings of the Conference on Language Modeling (COLM) 2024",
series = {COLM~'24},
month = {October},
year = "2024",
publisher = "Conference on Language Modeling",
address = "Philadelphia, PA"
}
@inproceedings{mirzakhmedova-etal-2024-touche23,
title = "The Touch{\'e}23-{V}alue{E}val Dataset for Identifying Human Values behind Arguments",
author = "Mirzakhmedova, Nailia and
Kiesel, Johannes and
Alshomary, Milad and
Heinrich, Maximilian and
Handke, Nicolas and
Cai, Xiaoni and
Barriere, Valentin and
Dastgheib, Doratossadat and
Ghahroodi, Omid and
SadraeiJavaheri, MohammadAli and
Asgari, Ehsaneddin and
Kawaletz, Lea and
Wachsmuth, Henning and
Stein, Benno",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1402",
pages = "16121--16134",
}
@inproceedings{sadraeijavaheri-etal-2024-transformers,
title = "Transformers for Bridging {P}ersian Dialects: Transliteration Model for Tajiki and {I}ranian Scripts",
author = "SadraeiJavaheri, MohammadAli and
Asgari, Ehsaneddin and
Rabiee, Hamid Reza",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1459",
pages = "16770--16775",
}
@inproceedings{ghahroodi-asgari-2024-hierarchyeverywhere,
title = "{H}ierarchy{E}verywhere at {S}em{E}val-2024 Task 4: Detection of Persuasion Techniques in Memes Using Hierarchical Text Classifier",
author = "Ghahroodi, Omid and
Asgari, Ehsaneddin",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.247",
doi = "10.18653/v1/2024.semeval-1.247",
pages = "1727--1732",
}
@inproceedings{abootorabi-etal-2024-aima,
title = "{AIMA} at {S}em{E}val-2024 Task 10: History-Based Emotion Recognition in {H}indi-{E}nglish Code-Mixed Conversations",
author = "Abootorabi, Mohammad Mahdi and
Ghazizadeh, Nona and
Dalili, Seyed Arshan and
Ghahramani Kure, Alireza and
Dehghani, Mahshid and
Asgari, Ehsaneddin",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.244",
doi = "10.18653/v1/2024.semeval-1.244",
pages = "1704--1710",
}
@inproceedings{ghahramani-kure-etal-2024-aima,
title = "{AIMA} at {S}em{E}val-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis",
author = "Ghahramani Kure, Alireza and
Dehghani, Mahshid and
Abootorabi, Mohammad Mahdi and
Ghazizadeh, Nona and
Dalili, Seyed Arshan and
Asgari, Ehsaneddin",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.243",
doi = "10.18653/v1/2024.semeval-1.243",
pages = "1698--1703",
abstract = "The SemEval-2024 Task 3 presents two subtasks focusing on emotion-cause pair extraction within conversational contexts. Subtask 1 revolves around the extraction of textual emotion-cause pairs, where causes are defined and annotated as textual spans within the conversation. Conversely, Subtask 2 extends the analysis to encompass multimodal cues, including language, audio, and vision, acknowledging instances where causes may not be exclusively represented in the textual data. Our proposed model for emotion-cause analysis is meticulously structured into three core segments: (i) embedding extraction, (ii) cause-pair extraction {\&} emotion classification, and (iii) cause extraction using QA after finding pairs. Leveraging state-of-the-art techniques and fine-tuning on task-specific datasets, our model effectively unravels the intricate web of conversational dynamics and extracts subtle cues signifying causality in emotional expressions. Our team, AIMA, demonstrated strong performance in the SemEval-2024 Task 3 competition. We ranked as the 10th in subtask 1 and the 6th in subtask 2 out of 23 teams.",
}
>>>>>>> upstream/master
@inproceedings{hasan-etal-2024-zero,
address = {Torino, Italia},
author = {Hasan, Md. Arid and Das, Shudipta and Anjum, Afiyat and Alam, Firoj and Anjum, Anika and Sarker, Avijit and Noori, Sheak Rashed Haider},
booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
date-modified = {2024-08-03 11:44:50 +0300},
editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},
month = may,
pages = {17808--17818},
publisher = {ELRA and ICCL},
title = {Zero- and Few-Shot Prompting with {LLM}s: A Comparative Study with Fine-tuned Models for {B}angla Sentiment Analysis},
year = {2024},
url = {https://aclanthology.org/2024.lrec-main.1549},
}
@inproceedings{alam-etal-2024-llms,
address = {St. Julian{'}s, Malta},
author = {Alam, Firoj and Chowdhury, Shammur Absar and Boughorbel, Sabri and Hasanain, Maram},
booktitle = {Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts},
date-modified = {2024-08-03 11:44:50 +0300},
editor = {Mesgar, Mohsen and Lo{\'a}iciga, Sharid},
month = mar,
pages = {27--33},
publisher = {Association for Computational Linguistics},
title = {{LLM}s for Low Resource Languages in Multilingual, Multimodal and Dialectal Settings},
year = {2024},
url = {https://aclanthology.org/2024.eacl-tutorials.5},
}
@inproceedings{abdelali-etal-2024-larabench,
address = {St. Julian{'}s, Malta},
author = {Abdelali, Ahmed and Mubarak, Hamdy and Chowdhury, Shammur and Hasanain, Maram and Mousi, Basel and Boughorbel, Sabri and Abdaljalil, Samir and El Kheir, Yassine and Izham, Daniel and Dalvi, Fahim and Hawasly, Majd and Nazar, Nizi and Elshahawy, Youssef and Ali, Ahmed and Durrani, Nadir and Milic-Frayling, Natasa and Alam, Firoj},
booktitle = {Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
date-modified = {2024-08-03 11:44:50 +0300},
editor = {Graham, Yvette and Purver, Matthew},
month = mar,
pages = {487--520},
publisher = {Association for Computational Linguistics},
title = {{LA}ra{B}ench: Benchmarking {A}rabic {AI} with Large Language Models},
year = {2024},
abstract = "Recent advancements in Large Language Models (LLMs) have significantly influenced the landscape of language and speech research. Despite this progress, these models lack specific benchmarking against state-of-the-art (SOTA) models tailored to particular languages and tasks. LAraBench addresses this gap for Arabic Natural Language Processing (NLP) and Speech Processing tasks, including sequence tagging and content classification across different domains. We utilized models such as GPT-3.5-turbo, GPT-4, BLOOMZ, Jais-13b-chat, Whisper, and USM, employing zero and few-shot learning techniques to tackle 33 distinct tasks across 61 publicly available datasets. This involved 98 experimental setups, encompassing {\textasciitilde}296K data points, {\textasciitilde}46 hours of speech, and 30 sentences for Text-to-Speech (TTS). This effort resulted in 330+ sets of experiments. Our analysis focused on measuring the performance gap between SOTA models and LLMs. The overarching trend observed was that SOTA models generally outperformed LLMs in zero-shot learning, with a few exceptions. Notably, larger computational models with few-shot learning techniques managed to reduce these performance gaps. Our findings provide valuable insights into the applicability of LLMs for Arabic NLP and speech processing tasks.",
url = {https://aclanthology.org/2024.eacl-long.30},
}
@inproceedings{dalvi-etal-2024-llmebench,
address = {St. Julians, Malta},
author = {Dalvi, Fahim and Hasanain, Maram and Boughorbel, Sabri and Mousi, Basel and Abdaljalil, Samir and Nazar, Nizi and Abdelali, Ahmed and Chowdhury, Shammur Absar and Mubarak, Hamdy and Ali, Ahmed and Hawasly, Majd and Durrani, Nadir and Alam, Firoj},
booktitle = {Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations},
date-modified = {2024-08-03 11:44:50 +0300},
editor = {Aletras, Nikolaos and De Clercq, Orphee},
month = mar,
pages = {214--222},
publisher = {Association for Computational Linguistics},
title = {{LLM}e{B}ench: A Flexible Framework for Accelerating {LLM}s Benchmarking},
year = {2024},
abstract = "The recent development and success of Large Language Models (LLMs) necessitate an evaluation of their performance across diverse NLP tasks in different languages. Although several frameworks have been developed and made publicly available, their customization capabilities for specific tasks and datasets are often complex for different users. In this study, we introduce the LLMeBench framework, which can be seamlessly customized to evaluate LLMs for any NLP task, regardless of language. The framework features generic dataset loaders, several model providers, and pre-implements most standard evaluation metrics. It supports in-context learning with zero- and few-shot settings. A specific dataset and task can be evaluated for a given LLM in less than 20 lines of code while allowing full flexibility to extend the framework for custom datasets, models, or tasks. The framework has been tested on 31 unique NLP tasks using 53 publicly available datasets within 90 experimental setups, involving approximately 296K data points. We open-sourced LLMeBench for the community (https://github.com/qcri/LLMeBench/) and a video demonstrating the framework is available online (https://youtu.be/9cC2m{\_}abk3A).",
url = {https://aclanthology.org/2024.eacl-demo.23},
}
@inproceedings{dimitrov-etal-2024-semeval,
address = {Mexico City, Mexico},
author = {Dimitrov, Dimitar and Alam, Firoj and Hasanain, Maram and Hasnat, Abul and Silvestri, Fabrizio and Nakov, Preslav and Da San Martino, Giovanni},
booktitle = {Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)},
date-modified = {2024-08-03 11:44:50 +0300},
editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Tayyar Madabushi, Harish and Da San Martino, Giovanni and Rosenthal, Sara and Ros{\'a}, Aiala},
month = jun,
pages = {2009--2026},
publisher = {Association for Computational Linguistics},
title = {{S}em{E}val-2024 Task 4: Multilingual Detection of Persuasion Techniques in Memes},
year = {2024},
url = {https://aclanthology.org/2024.semeval-1.275},
doi = {https://doi.org/10.18653/v1/2024.semeval-1.275},
}
@InProceedings{CheckThat:ECIR2024,
author="Barr{\'o}n-Cede{\~{n}}o, Alberto
and Alam, Firoj
and Chakraborty, Tanmoy
and Elsayed, Tamer
and Nakov, Preslav
and Przyby{\l}a, Piotr
and Stru{\ss}, Julia Maria
and Haouari, Fatima
and Hasanain, Maram
and Ruggeri, Federico
and Song, Xingyi
and Suwaileh, Reem",
editor="Goharian, Nazli
and Tonellotto, Nicola
and He, Yulan
and Lipani, Aldo
and McDonald, Graham
and Macdonald, Craig
and Ounis, Iadh",
title="The {CLEF}-2024 {C}heck{T}hat! {L}ab: Check-Worthiness, Subjectivity, Persuasion, Roles, Authorities, and Adversarial Robustness",
booktitle="Advances in Information Retrieval",
year="2024",
publisher="Springer Nature Switzerland",
NOaddress="Cham",
pages="449--458",
abstract="The first five editions of the CheckThat! lab focused on the main tasks of the information verification pipeline: check-worthiness, evidence retrieval and pairing, and verification. Since the 2023 edition, it has been focusing on new problems that can support the research and decision making during the verification process. In this new edition, we focus on new problems and ---for the first time--- we propose six tasks in fifteen languages (Arabic, Bulgarian, English, Dutch, French, Georgian, German, Greek, Italian, Polish, Portuguese, Russian, Slovene, Spanish, and code-mixed Hindi-English): Task 1 estimation of check-worthiness (the only task that has been present in all CheckThat! editions), Task 2 identification of subjectivity (a follow up of CheckThat! 2023 edition), Task 3 identification of persuasion (a follow up of SemEval 2023), Task 4 detection of hero, villain, and victim from memes (a follow up of CONSTRAINT 2022), Task 5 Rumor Verification using Evidence from Authorities (a first), and Task 6 robustness of credibility assessment with adversarial examples (a first). These tasks represent challenging classification and retrieval problems at the document and at the span level, including multilingual and multimodal settings.",
isbn="978-3-031-56069-9",
}
@InProceedings{clef-checkthat:2024-lncs,
author="Barr{\'o}n-Cede{\~{n}}o, Alberto
and Alam, Firoj
and Stru{\ss}, Julia Maria
and Nakov, Preslav
and Chakraborty, Tanmoy
and Elsayed, Tamer
and Przybyła, Piotr
and Caselli, Tommaso
and Da San Martino, Giovanni
and Haouari, Fatima
and Li, Chengkai
and Piskorski, Jakub
and Ruggeri, Federico
and Song, Xingyi
and Suwaileh, Reem",
title="Overview of the {CLEF}-2024 {CheckThat! Lab}: Check-Worthiness, Subjectivity, Persuasion, Roles,
Authorities and Adversarial Robustness",
editor="Goeuriot, Lorraine
and Mulhem, Philippe
and Quénot, Georges
and Schwab, Didier
and Soulier, Laure
and Di Nunzio, Giorgio Maria
and Galuščáková, Petra
and García Seco de Herrera, Alba
and Faggioli, Guglielmo
and Ferro, Nicola",
booktitle="Experimental IR Meets Multilinguality, Multimodality, and Interaction.
Proceedings of the Fifteenth International Conference of the CLEF Association (CLEF 2024)",
year="2024",
}
@inproceedings{hasanain-etal-2023-araieval,
address = {Singapore (Hybrid)},
author = {Hasanain, Maram and Alam, Firoj and Mubarak, Hamdy and Abdaljalil, Samir and Zaghouani, Wajdi and Nakov, Preslav and Da San Martino, Giovanni and Freihat, Abed},
booktitle = {Proceedings of ArabicNLP 2023},
date-modified = {2024-08-03 11:44:50 +0300},
editor = {Sawaf, Hassan and El-Beltagy, Samhaa and Zaghouani, Wajdi and Magdy, Walid and Abdelali, Ahmed and Tomeh, Nadi and Abu Farha, Ibrahim and Habash, Nizar and Khalifa, Salam and Keleg, Amr and Haddad, Hatem and Zitouni, Imed and Mrini, Khalil and Almatham, Rawan},
month = dec,
pages = {483--493},
publisher = {Association for Computational Linguistics},
title = {{A}r{AIE}val Shared Task: Persuasion Techniques and Disinformation Detection in {A}rabic Text},
year = {2023},
url = {https://aclanthology.org/2023.arabicnlp-1.44},
bdsk-url-2 = {https://doi.org/10.18653/v1/2023.arabicnlp-1.44},
bibtex_show={true}
}
@article{sajjad-neuron-survey,
title = "Neuron-level {I}nterpretation of {D}eep {NLP} {M}odels: {A} {S}urvey",
author = "Sajjad, Hassan and
Durrani, Nadir and
Dalvi, Fahim",
journal = "Transactions of the Association for Computational Linguistics",
volume = "11",
year = "2023",
address = "Cambridge, MA",
publisher = "MIT Press",
abstract = "The proliferation of deep neural networks in various domains has seen an increased need for interpretability of these models. Preliminary work done along this line and papers that surveyed such, are focused on high-level representation analysis. However, a recent branch of work has concentrated on interpretability at a more granular level of analyzing neurons within these models. In this paper, we survey the work done on neuron analysis including: i) methods to discover and understand neurons in a network, ii) evaluation methods, iii) major findings including cross architectural comparisons that neuron analysis has unraveled, iv) applications of neuron probing such as: controlling the model, domain adaptation etc., and v) a discussion on open issues and future research directions.",
}
@article{sajjad2023:csl,
address = {London, UK, UK},
author = {Sajjad, Hassan and Dalvi, Fahim and Durrani, Nadir and Nakov, Preslav},
issn = {0885-2308},
doi = {https://doi.org/10.1016/j.csl.2022.101429},
url = {https://www.sciencedirect.com/science/article/pii/S0885230822000596},
issue_date = {January 2023},
journal = {Computer Speech and Language},
number = {C},
publisher = {Academic Press Ltd.},
title = {On the Effect of Dropping Layers of Pre-trained Transformer Models},
volume = {77},
pages = {101429},
year = {2023},
area = {Transfer Learning}
}
@inproceedings{clef-checkthat:2023:task2,
author = {Galassi, Andrea and Ruggeri, Federico and Barr\'{o}n-Cede\~{n}o, Alberto and Alam, Firoj and Caselli, Tommaso and Kutlu, Mucahid and Struss, {Julia Maria} and Antici, Francesco and Hasanain, Maram and K{\"o}hler, Juliane and Korre, Katerina and Leistra, Folkert and Muti, Arianna and Siegel, Melanie and Turkmen. {Mehmet Deniz} and Wiegand, Michael and Zaghouani, Wajdi},
crossref = {clef2023-workingnotes},
date-modified = {2024-08-03 12:29:30 +0300},
title = {Overview of the {CLEF}-2023 {CheckThat}! Lab Task 2 on Subjectivity in News Articles},
year = {2023},
}
@inproceedings{clef-checkthat:2023:task3,
author = {Da San Martino, Giovanni and Alam, Firoj and Hasanain, Maram and Nandi, Rabindra Nath and Azizov, Dilshod and Nakov, Preslav},
booktitle = {Working Notes of {CLEF} 2023 - Conference and Labs of the Evaluation Forum},
crossref = {clef2023-workingnotes},
date-modified = {2024-08-03 12:30:07 +0300},
title = {Overview of the {CLEF}-2023 {CheckThat}! Lab Task 3 on Political Bias of News Articles and News Media},
year = {2023},
}
@inproceedings{barron2023clef,
author = {Barr{\'o}n-Cede{\~n}o, Alberto and Alam, Firoj and Caselli, Tommaso and Da San Martino, Giovanni and Elsayed, Tamer and Galassi, Andrea and Haouari, Fatima and Ruggeri, Federico and Stru{\ss}, Julia Maria and Nandi, Rabindra Nath and others},
booktitle = {European Conference on Information Retrieval},
organization = {Springer},
pages = {506--517},
title = {The clef-2023 checkthat! lab: Checkworthiness, subjectivity, political bias, factuality, and authority},
year = {2023},
}
@inproceedings{nandi-etal-2023-pseudo,
abstract = {One of the major challenges for developing automatic speech recognition (ASR) for low-resource languages is the limited access to labeled data with domain-specific variations. In this study, we propose a pseudo-labeling approach to develop a large-scale domain-agnostic ASR dataset. With the proposed methodology, we developed a 20k+ hours labeled Bangla speech dataset covering diverse topics, speaking styles, dialects, noisy environments, and conversational scenarios. We then exploited the developed corpus to design a conformer-based ASR system. We benchmarked the trained ASR with publicly available datasets and compared it with other available models. To investigate the efficacy, we designed and developed a human-annotated domain-agnostic test set composed of news, telephony, and conversational data among others. Our results demonstrate the efficacy of the model trained on psuedo-label data for the designed test-set along with publicly-available Bangla datasets. The experimental resources will be publicly available.https://github.com/hishab-nlp/Pseudo-Labeling-for-Domain-Agnostic-Bangla-ASR},
address = {Singapore},
author = {Nandi, Rabindra Nath and Menon, Mehadi and Muntasir, Tareq and Sarker, Sagor and Muhtaseem, Quazi Sarwar and Islam, Md. Tariqul and Chowdhury, Shammur and Alam, Firoj},
booktitle = {Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)},
date-modified = {2024-08-03 12:08:38 +0300},
editor = {Alam, Firoj and Kar, Sudipta and Chowdhury, Shammur Absar and Sadeque, Farig and Amin, Ruhul},
month = dec,
pages = {152--162},
publisher = {Association for Computational Linguistics},
title = {Pseudo-Labeling for Domain-Agnostic {B}angla Automatic Speech Recognition},
year = {2023},
url = {https://aclanthology.org/2023.banglalp-1.16},
doi = {https://doi.org/10.18653/v1/2023.banglalp-1.16},
}
@article{hasanain2023large,
author = {Hasanain, Maram and Ahmed, Fatema and Alam, Firoj},
journal = {arXiv preprint arXiv:2311.09812},
title = {Large language models for propaganda span annotation},
year = {2023},
selected={true},
preview={prop_example.png},
}
@inproceedings{hasanain-etal-2024-gpt,
address = {Torino, Italia},
author = {Hasanain, Maram and Ahmad, Fatema and Alam, Firoj},
booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
date-modified = {2024-08-03 11:44:50 +0300},
editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},
month = may,
pages = {2724--2744},
publisher = {ELRA and ICCL},
title = {Can {GPT}-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articles},
year = {2024},
url = {https://aclanthology.org/2024.lrec-main.244},
}
@inproceedings{hasanain-etal-2023-qcri,
title = "{QCRI} at {S}em{E}val-2023 Task 3: News Genre, Framing and Persuasion Techniques Detection Using Multilingual Models",
author = "Hasanain, Maram and
El-Shangiti, Ahmed and
Nandi, Rabindra Nath and
Nakov, Preslav and
Alam, Firoj",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.172",
doi = "10.18653/v1/2023.semeval-1.172",
pages = "1237--1244",
abstract = "Misinformation spreading in mainstream and social media has been misleading users in different ways. Manual detection and verification efforts by journalists and fact-checkers can no longer cope with the great scale and quick spread of misleading information. This motivated research and industry efforts to develop systems for analyzing and verifying news spreading online. The SemEval-2023 Task 3 is an attempt to address several subtasks under this overarching problem, targeting writing techniques used in news articles to affect readers{'} opinions. The task addressed three subtasks with six languages, in addition to three {``}surprise{''} test languages, resulting in 27 different test setups. This paper describes our participating system to this task. Our team is one of the 6 teams that successfully submitted runs for all setups. The official results show that our system is ranked among the top 3 systems for 10 out of the 27 setups.",
}
@inproceedings{barron2023clef,
title={The clef-2023 checkthat! lab: Checkworthiness, subjectivity, political bias, factuality, and authority},
author={Barr{\'o}n-Cede{\~n}o, Alberto and Alam, Firoj and Caselli, Tommaso and Da San Martino, Giovanni and Elsayed, Tamer and Galassi, Andrea and Haouari, Fatima and Ruggeri, Federico and Stru{\ss}, Julia Maria and Nandi, Rabindra Nath and others},
booktitle={European Conference on Information Retrieval},
pages={506--517},
year={2023},
organization={Springer}
}
@inproceedings{nadir:emnlp:2022,
title = "On the Transformation of Latent Space in Fine-Tuned NLP Models",
author = "Nadir Durrani and Hassan Sajjad and Fahim Dalvi and Firoj Alam",
booktitle = "The 2022 Conference on Empirical Methods in Natural Language Processing",
series={EMNLP~'22},
month = "dec",
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
}
@inproceedings{abdelali-2021-arabic-transformers,
title = "Post-hoc analysis of Arabic transformer models",
author = "Abdelali, Ahmed and
Durrani, Nadir and
Dalvi, Fahim and
Sajjad, Hassan",
booktitle = "Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
month = "dec",
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
abstract = "Arabic is a Semitic language which is widely spoken with many dialects. Given the success of pre-trained language models, many transformer models trained on Arabic and its dialects have surfaced. While there have been an extrinsic evaluation of these models with respect to downstream NLP tasks, no work has been carried out to analyze and compare their internal representations. We probe how linguistic information is encoded in the transformer models, trained on different Arabic dialects. We perform a layer and neuron analysis on the models using morphological tagging tasks for different dialects of Arabic and a dialectal identification task. Our analysis enlightens interesting findings such as: i) word morphology is learned at the lower and middle layers, ii) while syntactic dependencies are predominantly captured at the higher layers, iii) despite a large overlap in their vocabulary, the MSA-based models fail to capture the nuances of Arabic dialects, iv) we found that neurons in embedding layers are polysemous in nature, while the neurons in middle layers are exclusive to specific properties.",
}
@inproceedings{alam-etal-2022-survey,
title = "A Survey on Multimodal Disinformation Detection",
author = "Alam, Firoj and
Cresci, Stefano and
Chakraborty, Tanmoy and
Silvestri, Fabrizio and
Dimitrov, Dimiter and
Martino, Giovanni Da San and
Shaar, Shaden and
Firooz, Hamed and
Nakov, Preslav",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = "oct",
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.576",
pages = "6625--6643",
abstract = "Recent years have witnessed the proliferation of offensive content online such as fake news, propaganda, misinformation, and disinformation. While initially this was mostly about textual content, over time images and videos gained popularity, as they are much easier to consume, attract more attention, and spread further than text. As a result, researchers started leveraging different modalities and combinations thereof to tackle online multimodal offensive content. In this study, we offer a survey on the state-of-the-art on multimodal disinformation detection covering various combinations of modalities: text, images, speech, video, social media network structure, and temporal information. Moreover, while some studies focused on factuality, others investigated how harmful the content is. While these two components in the definition of disinformation {--} (i) factuality, and (ii) harmfulness {--}, are equally important, they are typically studied in isolation. Thus, we argue for the need to tackle disinformation detection by taking into account multiple modalities as well as both factuality and harmfulness, in the same framework. Finally, we discuss current challenges and future research directions.",
}
@inproceedings{sajjad-etal-2022-effect,
title = {Effect of Post-processing on Contextualized Word Representations},
author = {Sajjad, Hassan and Alam, Firoj and Dalvi, Fahim and Durrani, Nadir},
booktitle = {Proceedings of the 29th International Conference on Computational Linguistics},
month = "oct",
year = {2022},
address = {Gyeongju, Republic of Korea},
publisher = {International Committee on Computational Linguistics},
url = {https://aclanthology.org/2022.coling-1.277},
pages = {3127--3142},
area = {Representation Analysis}
}
@inproceedings{dalvi2022discovering,
title={Discovering Latent Concepts Learned in {BERT}},
author={Fahim Dalvi and Abdul Rafae Khan and Firoj Alam and Nadir Durrani and Jia Xu and Hassan Sajjad},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=POTMtpYI1xH}
}
@inproceedings{sajjad:naacl:2022,
title = "Analyzing Encoded Concepts in Transformer Language Models",
author = "Hassan Sajjad and Nadir Durrani and Fahim Dalvi and Firoj Alam and Abdul Rafae Khan and Jia Xu",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics",
series={NAACL~'22},
month = "Jul",
year = "2022",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
}
@inproceedings{alam-etal-2022-wanlp,
title = "Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic",
author = "Firoj Alam and Hamdy Mubarak and Wajdi Zaghouani and Giovanni Da San Martino and Preslav Nakov",
booktitle = "The Seventh Arabic Natural Language Processing Workshop (WANLP 2022) at EMNLP 2022",
month = "dec",
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
}
@inproceedings{shaar-etal-2022-assisting,
title = "Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document",
author = "Shaden Shaar and Nikola Georgiev and Firoj Alam and Giovanni Da San Martino and Aisha Mohamed and Preslav Nakov",
booktitle = "The 2022 Conference on Empirical Methods in Natural Language Processing",
month = "dec",
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
}