Question Generation using 🤗transformers

• Question Generation using 🤗transformers
  • Project Details
  • Initial experiments
    • answer aware question generation
    • answer extraction models
    • Multitask QA-QG
    • End-to-End question generation (answer agnostic)
  • Results
  • Requirements
  • Usage
    • Question Generation
    • Multitask QA-QG
    • End-to-end question generation (without answer supervision)
  • Fine-tuning
    • Data processing
    • training
    • Evaluation
  • Applications 🚀
  • Relevant papers

Project Details

Question generation is the task of automatically generating questions from a text paragraph. The most straight-forward way for this is answer aware question generation. In answer aware question generation the model is presented with the answer and the passage and asked to generate a question for that answer by considering the passage context. While there are many papers available for QG task, it's still not as mainstream as QA. One of the reasons is most of the earlier papers use complicated models/processing pipelines and have no pre-trained models available. Few recent papers, specifically UniLM and ProphetNet have SOTA pre-trained weights availble for QG but the usage seems quite complicated.

This project is aimed as an open source study on question generation with pre-trained transformers (specifically seq-2-seq models) using straight-forward end-to-end methods without much complicated pipelines. The goal is to provide simplified data processing and training scripts and easy to use pipelines for inference.

Initial experiments

Initial experiments are conducted using the SQuADv1 dataset and T5 model with different input processing formats as described below.

answer aware question generation

For answer aware models the input text can be processed in two ways.

1. prepend format:

Here the answer is simply added before the context and seperated by sep token. For example

42 [SEP] 42 is the answer to life, the universe and everything.

for T5 model the input is processed like this

answer: 42 context: 42 is the answer to life, the universe and everything.

2. highlight format

Here the answer span is highlighted within the text with special highlight tokens.

<hl> 42 <hl> is the answer to life, the universe and everything.

This idea is proposed in the "A Recurrent BERT-based Model for Question Generation" paper. See section 4.3

answer extraction models

As the answer aware models need answers for generating question, we need something which can extract answer like spans from the text. This can be done using various methods like NER, noun-phrase extarction etc. But here a model is trained to extract answer like spans, to see how it'll work. With T5, answer extarction is done using the text-to-format.

As the highlight format will need to know the position of extracted answer spans the input for answer extraction is processed as follows

1. split the text into senteces.
2. for each sentence that has answers, highlight the sentence with <hl> tokens.
3. for the target text join the answers in that sentence with <sep> tokens.

For example for this text

Python is a programming language. Created by Guido van Rossum and first released in 1991.

following examples will be created

Input text: <hl> Python is a programming language. <hl> Created by Guido van Rossum and first released in 1991.

target text: Python <sep>

and

Input text: Python is a programming language. <hl> Created by Guido van Rossum and first released in 1991 <hl>.

target text: Guido van Rossum <sep> 1991 <sep>

At inference time the text is split into sentences and each sentence is highlighted.

Multitask QA-QG

For answer aware question generation we usually need 3 models, first which will extract answer like spans, second model will generate question on that answer and third will be a QA model which will take the question and produce an answer, then we can compare the two answers to see if the generated question is correct or not.

Having 3 models for single task is lot of complexity, so goal is to create a multi-task model which can do all of these 3 tasks

1. extract answer like spans
2. generate question based on the answer
3. QA

T5 model is fine-tuned in multi-task way using task prefixes as described in the paper.

End-to-End question generation (answer agnostic)

In end-to-end question generation the model is aksed to generate questions without providing the answers. This paper discusses these ideas in more detail. Here the T5 model is trained to generate multiple questions simultaneously by just providing the context. The questions are seperated by the <sep> token. Here's how the examples are processed

input text: Python is a programming language. Created by Guido van Rossum and first released in 1991.

target text: Who created Python ? <sep> When was python released ? <sep>

All the training details can be found in this wandb project

Results

Results on the SQuAD1.0 dev set using above approaches. For decoding, beam search with num_beams 4 is used with max decoding length set to 32.

For multitask qa-qg models the EM and F1 scores are privded as QA-EM and QA-F1.

The nlg-eval package is used for calculating the metrics.

Name	BLEU-4	METEOR	ROUGE-L	QA-EM	QA-F1	QG-FORMAT
t5-base-qg-hl	21.3226	27.0854	43.5962	-	-	highlight
t5-base-qa-qg-hl	21.0141	26.9113	43.2484	82.46	90.272	highlight
t5-small-qa-qg-hl	18.9872	25.2217	40.7893	76.121	84.904	highlight
t5-small-qg-hl	18.5921	24.9915	40.1886	-	-	highlight
t5-small-qg-prepend	18.2791	24.6722	39.958	-	-	prepend

Requirements

python -m pip install git+https://github.com/patil-suraj/question_generation.git

after installing nltk do

python -m nltk.downloader punkt

Usage

Use the pipeline whch mimics 🤗transformers pipeline for easy inference.

The pipeline is divided into 3 tasks

1. question-generation: for single task question generation models.
2. multitask-qa-qg: for multi-task qa,qg models.
3. e2e-qg: for end-to-end question generation.

Question Generation

from question_generation import pipeline

nlp = pipeline("question-generation")
nlp("42 is the answer to life, the universe and everything.")
=> [{'answer': '42', 'question': 'What is the answer to life, the universe and everything?'}]

prepend format

nlp = pipeline("question-generation", model="valhalla/t5-small-qg-prepend", qg_format="prepend")
nlp("42 is the answer to life, the universe and everything.")
=> [{'answer': '42 ', 'question': 'What is the answer to life, the universe, and everything?'}]

Multitask QA-QG

nlp = pipeline("multitask-qa-qg")

# to generate questions simply pass the text
nlp("42 is the answer to life, the universe and everything.")
=> [{'answer': '42', 'question': 'What is the answer to life, the universe and everything?'}]

# for qa pass a dict with "question" and "context"
nlp({
    "question": "What is 42 ?",
    "context": "42 is the answer to life, the universe and everything."
})
=> 'the answer to life, the universe and everything'

End-to-end question generation (without answer supervision)

nlp = pipeline("e2e-qg")
nlp("Python is a programming language. Created by Guido van Rossum and first released in 1991.")
=> [
    'What is a programming language?',
    'Who created Python?',
    'When was Python first released?'
]

By default both pipelines will use the t5-small* models, to use the other models pass the path through model paramter.

By default the question-generation pipeline will download the valhalla/t5-small-qg-hl model with highlight qg format. If you want to use prepend format then provide the path to the prepend model and set qg_format to "prepend". For extracting answer like spans it uses valhalla/t5-small-qa-qg-hl model, you can provide a different model through ans_model parameter.

The multitask-qa-qg model is for multitask models which can extract answer like spans, do qg and qa, so it won't need seperate ans_model. By default valhalla/t5-small-qa-qg-hl model is used with highlight format. If you want to use prepend format then provide the path to the prepend model and set qg_format to "prepend"

The e2e-qg pipeline is for end-to-end question generation. These models can generate multiple questions simultaneously without answer supervision. By default it uses valhalla/t5-small-e2e-qg

Fine-tuning

Data processing

To support different data formats the trainer expects pre-processed cached dataset, so you can process the data the way you want. The cached dataset should be saved using torch.save and it should return a dict with source_ids, target_ids, attention_mask keys from __getitem__.

• source_ids: encoded source text
• target_ids: encoded target text
• attention_mask: attention mask for the source_ids

The T2TDataCollator takes care of preparing right input_ids and labels. It also trims the batches dynamically to remove excessive padding tokens, to speed up the training.

The data/squad_multitask containes the modifed SQuAD dataset for answer aware question generation (using both prepend and highlight formats), question answering (text-to-text), answer extraction and end-to-end question generation. This dataset can be loaded using the awesome 🤗nlp library, this makes processing very easy.

To process and cache the dataset use prepare_data.py script. It will load the correct tokenizer depending on the model_type argument. It adds two new tokens <sep> and <hl> to the tokenizer and saves it at {model_type}_qg_tokenizer path. You should pass this tokenizer to the fine-tuning script.

The datasets will be saved in data/ directory. You should provide filenames using train_file_name and valid_file_name arguments.

process data for single task question generation with highlight_qg_format

python -m question_generation.prepare_data.py \
    --task qg \
    --model_type t5 \
    --dataset_path data/squad_multitask/ \
    --qg_format highlight_qg_format \
    --max_source_length 512 \
    --max_target_length 32 \
    --train_file_name train_data_qg_hl_t5.pt \
    --valid_file_name valid_data_qg_hl_t5.pt \

process data for multi-task qa-qg with highlight_qg_format

valid_for_qg_only argument is used to decide if the validation set should only contain data for qg task. For my multi-task experiments I used validation data with only qg task so that the eval loss curve can be easly compared with other single task models

python -m question_generation.prepare_data.py \
    --task multi \
    --valid_for_qg_only \ 
    --model_type t5 \
    --dataset_path data/squad_multitask/ \
    --qg_format highlight_qg_format \
    --max_source_length 512 \
    --max_target_length 32 \
    --train_file_name train_data_qa_qg_hl_t5.pt \
    --valid_file_name valid_data_qg_hl_t5.pt \

process dataset for end-to-end question generation

python -m question_generation.prepare_data.py \
    --task e2e_qg \
    --valid_for_qg_only \ 
    --model_type t5 \
    --dataset_path data/squad_multitask/ \
    --qg_format highlight_qg_format \
    --max_source_length 512 \
    --max_target_length 32 \
    --train_file_name train_data_e2e_qg_t5.pt \
    --valid_file_name valid_data_e2e_qg_t5.pt \

training

Use the run_qg.py script to start training. It uses transformers Trainer class for training the models.

python -m question_generation.run_qg.py \
    --model_name_or_path t5-small \
    --model_type t5 \
    --tokenizer_name_or_path t5_qg_tokenizer \
    --output_dir t5-small-qg-hl \
    --train_file_path data/train_data_qg_hl_t5.pt \
    --valid_file_path data/valid_data_qg_hl_t5.pt \
    --per_device_train_batch_size 32 \
    --per_device_eval_batch_size 32 \
    --gradient_accumulation_steps 8 \
    --learning_rate 1e-4 \
    --num_train_epochs 10 \
    --seed 42 \
    --do_train \
    --do_eval \
    --evaluate_during_training \
    --logging_steps 100

or if you want to train it from script or notebook then

from question_generation import run_qg

args_dict = {
    "model_name_or_path": "t5-small",
    "model_type": "t5",
    "tokenizer_name_or_path": "t5_qg_tokenizer",
    "output_dir": "t5-small-qg-hl",
    "train_file_path": "data/train_data_qg_hl_t5.pt",
    "valid_file_path": "data/valid_data_qg_hl_t5.pt",
    "per_device_train_batch_size": 32,
    "per_device_eval_batch_size": 32,
    "gradient_accumulation_steps": 8,
    "learning_rate": 1e-4,
    "num_train_epochs": 10,
    "seed": 42,
    "do_train": True,
    "do_eval": True,
    "evaluate_during_training": True,
    "logging_steps": 100
}

# start training
run_qg(args_dict)

Evaluation

Use the eval.py script for evaluting the model.

python -m question_generation.eval.py \
    --model_name_or_path t5-base-qg-hl \
    --valid_file_path valid_data_qg_hl_t5.pt \
    --model_type t5 \
    --num_beams 4 \
    --max_decoding_length 32 \
    --output_path hypothesis_t5-base-qg-hl.txt

This will save the output at {output_path} file.

To calculate the metrics install the nlg-eval package and run

nlg-eval --hypothesis=hypothesis_t5-base-qg-hl.txt --references=data/references.txt --no-skipthoughts --no-glove 

Applications 🚀

1. A simple Trivia Quiz on topics of your choice -
   Medium article and its Colab Notebook
2. Autocards, Accelerating learning through machine-generated flashcards

Relevant papers

• https://arxiv.org/abs/1906.05416
• https://www.aclweb.org/anthology/D19-5821/
• https://arxiv.org/abs/2005.01107v1