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Machine Reading Comprehension
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Machine Reading Comprehension is one of the key problems in Natural Language Understanding, where the task is to read and comprehend a given text passage, and then answer questions based on it.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/gooofy/zbrain
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/iamNCJ/DUMA-pytorch-lightning
https://github.com/iflytek/cino
https://github.com/jeongukjae/KR-BERT-SimCSE
https://github.com/jinmang2/RetroReader
https://github.com/lduml/blog
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/libertatis/mrc-cbt
https://github.com/manliu1225/mrc-for-flat-nested-ner
https://github.com/microsoft/MSMARCO-Conversational-Search
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-Document-Ranking
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-OpenKP
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-Passage-Ranking
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-Question-Answering
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/OpenKP
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/TREC-2019-Deep-Learning
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/mpandeydev/SDnetmod
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/nlpdata/c3
Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension
https://github.com/om00839/machine-suneung
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/princeton-nlp/helmet
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/saketdingliwal/Few-Shot-DST
From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap
https://github.com/sharejing/BiPaR
https://github.com/shuhewang1998/gpt-ner
https://github.com/thanhlt998/mrc-ner
https://github.com/vijayakuruba/IFT6010_Applying_Answer-Clue-Style_Approach_to_VQG
Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
https://github.com/wenhuchen/gnn-tabfact
NumNet: Machine Reading Comprehension with Numerical Reasoning
https://github.com/xycforgithub/Airbnb-Comparison
Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension
https://github.com/yly-revive/chainer-mreader
Reinforced Mnemonic Reader for Machine Reading Comprehension
https://github.com/yongbowin/MSMARCO-Question-Answering_annotation
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/yongbowin/OpenKP_annotation
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/yongbowin/san_mrc_annotation
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/zhayefei/off-topic-GCBiA
Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection
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