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Sogou Machine Reading Comprehension Toolkit

Introduction

The Sogou Machine Reading Comprehension (SMRC) toolkit was designed for the fast and efficient development of modern machine comprehension models, including both published models and original prototypes.

Toolkit Architecture

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Installation

$ git clone https://github.com/sogou/SMRCToolkit.git
$ cd SMRCToolkit
$ pip install [-e] .

Option -e makes your installation editable, i.e., it links it to your source directory

This repo was tested on Python 3 and Tensorflow 1.12

Quick Start

To train a Machine Reading Comprehension model, please follow the steps below.

For SQuAD1.0, you can download a dataset with the following commands.

$ wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json
$ wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json
$ wget https://nlp.stanford.edu/data/glove.840B.300d.zip #used in DrQA
$ unzip glove.840B.300d.zip

Prepare the dataset reader and evaluator.

train_file = data_folder + "train-v1.1.json"
dev_file = data_folder + "dev-v1.1.json"
reader = SquadReader()
train_data = reader.read(train_file)
eval_data = reader.read(dev_file)
evaluator = SquadEvaluator(dev_file)

Build a vocabulary and load the pretrained embedding.

vocab = Vocabulary(do_lowercase=False)
vocab.build_vocab(train_data + eval_data, min_word_count=3, min_char_count=10)
word_embedding = vocab.make_word_embedding(embedding_folder+"glove.840B.300d.txt")

Use the feature extractor,which is only necessary when using linguistic features.

feature_transformer = FeatureExtractor(features=['match_lemma','match_lower','pos','ner','context_tf'],
build_vocab_feature_names=set(['pos','ner']),word_counter=vocab.get_word_counter())
train_data = feature_transformer.fit_transform(dataset=train_data)
eval_data = feature_transformer.transform(dataset=eval_data)

Build a batch generator for training and evaluation,where additional features and a feature vocabulary are necessary when a linguistic feature is used.

train_batch_generator = BatchGenerator(vocab,train_data, training=True, batch_size=32, \
    additional_fields = feature_transformer.features,feature_vocab=feature_transformer.vocab)
eval_batch_generator = BatchGenerator(vocab,eval_data, batch_size=32, \
    additional_fields = feature_transformer.features, feature_vocab=feature_transformer.vocab)

Import the built-in model and compile the training operation, call functions such as train_and_evaluate for training and evaluation.

model = DrQA(vocab, word_embedding, features=feature_transformer.features,\
 feature_vocab=feature_transformer.vocab)
model.compile()
model.train_and_evaluate(train_batch_generator, eval_batch_generator, evaluator, epochs=40, eposides=2)

All of the codes are provided using built-in models running on different datasets in the examples. You can check these for details. Example of model saving and loading.

Modules

data
- vocabulary.py: Vocabulary building and word/char index mapping
- batch_generator.py: Mapping words and tags to indices, padding length-variable features, transforming all of the features into tensors, and then batching them
dataset_reader
- squad.py: Dataset reader and evaluator (from official code) for SQuAD 1.0
- squadv2.py : Dataset reader and evaluator (from official code) for SQuAD 2.0
- coqa.py : Dataset reader and evaluator (from official code) for CoQA
- cmrc.py :Dataset reader and evaluator (from official code) for CMRC
examples
- Examples for running different models, where the specified data path should provided to run the examples
model
- Base class and subclasses of models, where any model should inherit the base class
- Built-in models such as BiDAF, DrQA, and FusionNet
nn
- similarity_function.py: Similarity functions for attention, e.g., dot_product, trilinear, and symmetric_nolinear
- attention.py: Attention functions such as BiAttention, Trilinear and Uni-attention
- ops: Common ops
- recurrent: Wrappers for LSTM and GRU
- layers: Layer base class and commonly used layers
utils
- tokenizer.py: Tokenizers that can be used for both English and Chinese
- feature_extractor: Extracting linguistic features used in some papers, e.g., POS, NER, and Lemma
libraries
- Bert is included in this toolkit with the code from the official source code.

Custom Model and Dataset

Custom models can easily be added with the description in the tutorial.
A new dataset can easily be supported by implementing a Custom Dataset Reader and Evaluator.

Performance

F1/EM score on SQuAD 1.0 dev set

Model	toolkit implementation	original paper
BiDAF	77.3/67.7	77.3/67.7
BiDAF+ELMo	81.0/72.1	-
IARNN-Word	73.9/65.2	-
IARNN-hidden	72.2/64.3	-
DrQA	78.9/69.4	78.8/69.5
DrQA+ELMO	83.1/74.4	-
R-Net	79.3/70.8	79.5/71.1
BiDAF++	78.6/69.2	-/-
FusionNet	81.0/72.0	82.5/74.1
QANet	80.8/71.8	82.7/73.6
BERT-Base	88.3/80.6	88.5/80.8

F1/EM score on SQuAD 2.0 dev set

Model	toolkit implementation	original paper
BiDAF	62.7/59.7	62.6/59.8
BiDAF++	64.3/61.8	64.8/61.9
BiDAF++ + ELMo	67.6/64.8	67.6/65.1
BERT-Base	75.9/73.0	75.1/72.0

F1 score on CoQA dev set

Model	toolkit implementation	original paper
BiDAF++	71.7	69.2
BiDAF++ + ELMo	74.5	69.2
BERT-Base	78.6	-
BERT-Base+Answer Verification	79.5	-

Contact information

For help or issues using this toolkit, please submit a GitHub issue.

Citation

If you use this toolkit in your research, please use the following BibTex Entry

@ARTICLE{2019arXiv190311848W,
       author = {{Wu}, Jindou and {Yang}, Yunlun and {Deng}, Chao and {Tang}, Hongyi and
         {Wang}, Bingning and {Sun}, Haoze and {Yao}, Ting and {Zhang}, Qi},
        title = "{Sogou Machine Reading Comprehension Toolkit}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computation and Language},
         year = "2019",
        month = "Mar",
          eid = {arXiv:1903.11848},
        pages = {arXiv:1903.11848},
archivePrefix = {arXiv},
       eprint = {1903.11848},
 primaryClass = {cs.CL},
       adsurl = {https://ui.adsabs.harvard.edu/\#abs/2019arXiv190311848W},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

License

Apache-2.0