AwesomeMRC

*working in progress

This repo is our research summary and playground for MRC. More features are coming.

Summary

Looking for a comprehensive and comparative review on MRC? check out our new survey paper: Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond (2020).

In this work, MRC model is regarded as a two-stage Encoder-Decoder architecture. Our empirical analysis is shared in this repo.

Encoder:

1. Language Units

Subword-augmented Embedding for Cloze Reading Comprehension

Effective Subword Segmentation for Text Comprehension

2. Linguistic Knowledge

Semantics-aware BERT for language understanding

SG-Net: Syntax-Guided Machine Reading Comprehension

LIMIT-BERT : Linguistic Informed Multi-Task BERT

3. Commonsense Injection

Multi-choice Dialogue-Based Reading Comprehension with Knowledge and Key Turns

4. Contextualized language models (CLMs) for MRC:

Decoder:

The implementation is based on Transformers v2.3.0.

As part of the techniques in our Retro-Reader paper:

Retrospective Reader for Machine Reading Comprehension

Answer Verification

1) Multitask-style verification

We evaluate different loss functions

cross-entropy (run_squad_av.py)

binary cross-entropy (run_squad_av_bce.py)

mse regression (run_squad_avreg.py)

2) External verification

Train an external verifier (run_cls.py)

Matching Network

Cross Attention (run_squad_seq_trm.py)

Matching Attention (run_squad_seq_sc.py)

Related Work:

Modeling Multi-turn Conversation with Deep Utterance Aggregation

DCMN+: Dual Co-Matching Network for Multi-choice Reading Comprehension

Dual Multi-head Co-attention for Multi-choice Reading Comprehension

Semantics-Aware Inferential Network for Natural Language Understanding

Answer Dependency

Model answer dependency (start + seq -> end) (run_squad_dep.py)

Retrospective Reader

1. train a sketchy reader (sh_albert_cls.sh)

2. train an intensive reader (sh_albert_av.sh)

3. rear verification: merge the prediction for final answer (run_verifier.py)

SQuAD 2.0 Dev Results:

{
"exact": 87.75372694348522, 
"f1": 90.91630165754992, 
"total": 11873, 
"HasAns_exact": 83.1140350877193, 
"HasAns_f1": 89.4482539777485, 
"HasAns_total": 5928, 
"NoAns_exact": 92.38015138772077, 
"NoAns_f1": 92.38015138772077, 
"NoAns_total": 5945
}

Citation

@article{zhang2020retrospective,
  title={Retrospective reader for machine reading comprehension},
  author={Zhang, Zhuosheng and Yang, Junjie and Zhao, Hai},
  journal={arXiv preprint arXiv:2001.09694},
  year={2020}
}

Related Records (best)

CMRC 2017: The best single model (2017).

SQuAD 2.0: The best among all submissions (both single and ensemble settings) & The first to surpass human benchmark on both EM and F1 scores with a single model (2019).

SNLI: The best among all submissions (2019-2020).

RACE: The best among all submissions (2019).

GLUE: The 3rd best among all submissions (early 2019).

Contact

Feel free to email zhangzs [at] sjtu.edu.cn if you have any questions.