Important paper implementations for Question Answering using PyTorch

BERT based pretrained model using SQuAD 2.0 Dataset for Question-Answering

NLP-CHATBOT

Visual Question Answering System

Question answering system developed using seq2seq modeling - The SQuAD dataset.

Implementation of a Dynamic Coattention Network proposed by Xiong et al.(2017) for Question Answering, learning to find answers spans in a document, given a question, using the Stanford Question Answering Dataset (SQuAD2.0).

A project about fine-tuning bert-base-uncased model for reading comprehension tasks.

MRC question and answer approach using NLP and machine learning techniques

Question Answering using BERT pre-trained model and fine-tuning it on various datasets (SQuAD, TriviaQA, NewsQ, Natural Questions, QuAC)

A context based question answering system trained on the SQUAD 2.0 dataset

Initially implement Document-Retrieval-System with SBERT embeddings and evaluate it in CORD-19 dataset. Afterwards, fine tune BERT model with SQuAD.v2 dataset so as to evaluate it in Question Answering task.

Sentence Bert for Question-Answering on COVID-19 Open Research Dataset (CORD-19)

[EMNLP 2024] Official Implementation of DisGeM: Distractor Generation for Multiple Choice Question with Span Masking

Sentiment Classifier using: Softmax-Regression, Feed-Forward Neural Network, Bidirectional stacked LSTM/GRU Recursive Neural Network, fine-tuning on BERT pre-trained model. Question Answering using BERT pre-trained model and fine-tuning it on various datasets (SQuAD, TriviaQA, NewsQ, Natural Questions, QuAC)

Topic+QA pipeLine

Tutorial of Question Answering using SQuAD in English and Spanish with BERT and BiDAF.

A personal implementation of "Adversarial Examples for Evaluating Reading Comprehension Systems".

Machine Comprehension on Squad Dataset using Match-LSTM + Ans-Ptr Network

A large scale dataset for Question Answering in Ukrainian