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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/aied2021TRMRC/AIED_2021_TRMRC_code
공식 코드Automatic Task Requirements Writing Evaluation via Machine Reading Comprehension
https://github.com/aitmlouk/fedn-client-fedqas-tf
공식 코드FedQAS: Privacy-aware machine reading comprehension with federated learning
https://github.com/alab-nii/eval_vl_glue
공식 코드Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language Models
https://github.com/alibaba/EasyNLP
공식 코드KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering
https://github.com/applicaai/multi-property-extraction
공식 코드From Dataset Recycling to Multi-Property Extraction and Beyond
https://github.com/baidu/DuReader/tree/master/DuReader-Robust
공식 코드https://github.com/bangliu/ACS-QG
공식 코드Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
https://github.com/benywon/ComQA
공식 코드ComQA:Compositional Question Answering via Hierarchical Graph Neural Networks
https://github.com/benywon/ReCO
공식 코드ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion
https://github.com/beyondguo/genius
공식 코드https://github.com/boyuanzheng010/SemEval2021-Reading-Comprehension-of-Abstract-Meaning
공식 코드SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning
https://github.com/bright2013/CorefAwareMRC
공식 코드Tracing Origins: Coreference-aware Machine Reading Comprehension
https://github.com/cather-chen/logical-reasoning-graph
공식 코드https://github.com/chen-gx/mprompt
공식 코드MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension
https://github.com/clips/clicr
공식 코드CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehension
https://github.com/cooelf/AwesomeMRC
공식 코드https://github.com/cooelf/SemBERT
공식 코드https://github.com/cooelf/subMrc
공식 코드https://github.com/cooelf/subword_seg
공식 코드https://github.com/copenlu/spanex
공식 코드https://github.com/cos4007/grape_demonstrations
공식 코드https://github.com/damithdr/questionanswering
공식 코드https://github.com/damo-nlp-sg/pmr
공식 코드https://github.com/dariad/rczoo
공식 코드https://github.com/datascienceuibk/arabicaqa
공식 코드ArabicaQA: A Comprehensive Dataset for Arabic Question Answering
https://github.com/datascienceuibk/chroniclingamericaqa
공식 코드https://github.com/dayihengliu/CRQDA
공식 코드Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space
https://github.com/dayuyang1999/zeqr
공식 코드ZeQR: Zero-shot Query Reformulation for Conversational Search
https://github.com/dkalpakchi/embrace
공식 코드https://github.com/dpappas/Data-Augmentation-for-Biomedical-Factoid-Question-Answering
공식 코드https://github.com/ericlee8/multi-party-dialogue-mrc
공식 코드https://github.com/facebookresearch/belebele
공식 코드The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants
https://github.com/fcihraeipnusnacwh/mrc-ce
공식 코드Large-scale Multi-granular Concept Extraction Based on Machine Reading Comprehension
https://github.com/freesunshine0316/nary-grn
공식 코드Tackling Graphical NLP problems with Graph Recurrent Networks
https://github.com/geekjuruo/beer2
공식 코드Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.