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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/ACL2020Anonymous/SymBERT
공식 코드Symmetric Regularization based BERT for Pair-wise Semantic Reasoning
https://github.com/AkariAsai/logic_guided_qa
공식 코드Logic-Guided Data Augmentation and Regularization for Consistent Question Answering
https://github.com/Alab-NII/mrc-heuristics
공식 코드https://github.com/BinWang28/InstructDS
공식 코드https://github.com/CLARIN-PL/PUGG
공식 코드Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction
https://github.com/CLUEbenchmark/CLUE
공식 코드https://github.com/CLUEbenchmark/FewCLUE
공식 코드https://github.com/CongSun-dlut/BioBERT-MRC
공식 코드Biomedical named entity recognition using BERT in the machine reading comprehension framework
https://github.com/DancingSoul/NQ_BERT-DM
공식 코드Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension
https://github.com/DigitalPhonetics/reading-comprehension
공식 코드https://github.com/Disiok/poetry-seq2seq
공식 코드Hierarchical Attention: What Really Counts in Various NLP Tasks
https://github.com/Francois-Aubet/EQuANt
공식 코드https://github.com/GeekDream-x/IDOL
공식 코드IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning
https://github.com/HIT-SCIR/Molweni
공식 코드https://github.com/HongyuGong/RCM-Question-Answering
공식 코드Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension
https://github.com/Hzfinfdu/MPMP
공식 코드Multitask Pre-training of Modular Prompt for Chinese Few-Shot Learning
https://github.com/IBM/multidoc2dial
공식 코드MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents
https://github.com/INK-USC/mrc-explanation
공식 코드Teaching Machine Comprehension with Compositional Explanations
https://github.com/INK-USC/nl-explanation
공식 코드Teaching Machine Comprehension with Compositional Explanations
https://github.com/KLUE-benchmark/KLUE
공식 코드https://github.com/KazutoshiShinoda/VQAG
공식 코드https://github.com/Microsoft/SDNet
공식 코드SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/NKU-IIPLab/BMRC
공식 코드Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction
https://github.com/NoviScl/AdvRACE
공식 코드Benchmarking Robustness of Machine Reading Comprehension Models
https://github.com/PaddlePaddle/Research/tree/master/NLP/EMNLP2019-AKGCM
공식 코드Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs
https://github.com/ShannonAI/ChineseBert
공식 코드ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
https://github.com/ShannonAI/CorefQA
공식 코드https://github.com/ShannonAI/dice_loss_for_NLP
공식 코드https://github.com/ShannonAI/mrc-for-dependency-parsing
공식 코드https://github.com/ShannonAI/mrc-for-flat-nested-ner
공식 코드https://github.com/SkelterLabsInc/JaQuAD
공식 코드JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension
https://github.com/SparkJiao/Self-Training-MRC
공식 코드A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction
https://github.com/SparkJiao/retrieval-based-mrc-pretraining
공식 코드REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training
https://github.com/Spico197/Mirror
공식 코드Mirror: A Universal Framework for Various Information Extraction Tasks
https://github.com/THU-KEG/MRCEval
공식 코드MRCEval: A Comprehensive, Challenging and Accessible Machine Reading Comprehension Benchmark
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.