tangbinh / question-answering

Overview

This repository contains curated PyTorch implementations of several question answering systems evaluated on SQuAD:

• Reading Wikipedia to Answer Open-Domain Questions (DrQA). Compared to the code in facebookresearch/DrQA, the implementation in this repository doesn't have the document retriever, making it cleaner and much more light-weighted. The F1 score on the validation set for this implementation is 77.6% (the official F1 score is 78.8%).

• Bidirectional Attention Flow for Machine Comprehension (BiDAF). In contrast to the original paper, the BiDAF implementation uses 300-dimensional word embeddings and doesn't have a highway network to combine character embeddings and word embeddings. The embeddings of the most frequent 1000 words are also fine-tuned for better performance, achieving an F1 score of 78.4% and an EM score of 69.3% (the official F1 score and EM score is 77.3% and 67.7%, respectively).

Installation

The code was written for Python 3.6 or higher, and it has been tested with PyTorch 0.4.1. Other dependencies are listed in requirements.txt. Training is only available with GPU. To get started, try to clone the repository

git clone https://github.com/tangbinh/question-answering
cd question-answering
pip install -r requirements.txt

There are two tokenizers included in this implementation, CoreNLPTokenizer and SpacyTokenizer. Although spaCy is faster, its tokenizer actually results in more questions having no answers when mapped from texts to tokens. The default setting therefore requires CoreNLPTokenizer, so please download the Stanford CoreNLP jars and include them in your CLASSPATH by following these commands:

wget "http://nlp.stanford.edu/software/stanford-corenlp-full-2017-06-09.zip"
unzip "stanford-corenlp-full-2017-06-09.zip"
rm "stanford-corenlp-full-2017-06-09.zip"
mv "stanford-corenlp-full-2017-06-09" "corenlp"
export CLASSPATH=corenlp/*:$CLASSPATH

Preprocessing

If you haven't downloaded SQuAD or GloVe, it might be easier for you to just run

bash download.sh

Then, the following command helps tokenize the downloaded datasets, extract features such as part-of-speech tagging, and build a dictionary using all CPUs available in your machine:

python preprocess.py --data data/squad --embed-path wordvec/glove/glove.840B.300d.txt --restrict-vocab --num-characters 300

Training

To get started with training a model on SQuAD, you might find the following commands helpful:

python train.py --arch drqa --embed-path wordvec/glove/glove.840B.300d.txt --checkpoint-dir checkpoints/drqa --log-file logs/drqa.log
python train.py --arch bidaf --embed-path wordvec/glove/glove.840B.300d.txt --checkpoint-dir checkpoints/bidaf --log-file logs/bidaf.log --lr 0.01

Prediction

When the training is done, you can make predictions and run the official evaluation script:

python predict.py --input data/dev.json --output predictions/dev-pred.json --feature-dict data/feature_dict.json --checkpoint checkpoints/drqa/checkpoint_best.pt
python evaluate.py data/dev-v1.1.json predictions/dev-pred.json

Interactive

As in the official implementation, it's possible to have an interactive environment for evaluation:

python interactive.py --checkpoint checkpoints/drqa/checkpoint_best.pt

>>> context = "Mary had a little lamb, whose fleece was white as snow. And everywhere that Mary went the lamb was sure to go."
>>> question = "What color is Mary's lamb?"
>>> answer(context, question)
+------+-------+--------------------+
| Rank |  Span |       Score        |
+------+-------+--------------------+
|  1   | white | 0.8701031804084778 |
+------+-------+--------------------+
Time: 0.2440