https://github.com/weiyifan1023/MVGE
공식 코드Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question
Papers with Code 아카이브
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/witcherleo/knowing-how-knowing-that
공식 코드Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals
https://github.com/wulinjuan/ssdm_mrc
공식 코드https://github.com/wzhouad/context-faithful-llm
공식 코드https://github.com/xiangyue9607/CliniRC
공식 코드Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset
https://github.com/xingdi-eric-yuan/imrc_graph_public
공식 코드Interactive Machine Comprehension with Dynamic Knowledge Graphs
https://github.com/xingdi-eric-yuan/imrc_public
공식 코드Interactive Machine Comprehension with Information Seeking Agents
https://github.com/xingdi-eric-yuan/qait_public
공식 코드https://github.com/xiye17/EvalQAExpl
공식 코드Connecting Attributions and QA Model Behavior on Realistic Counterfactuals
https://github.com/xufangzhi/logiformer
공식 코드Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning
https://github.com/xycforgithub/MultiTask-MRC
공식 코드Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension
https://github.com/yibowang214/multiner
공식 코드Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach
https://github.com/yilin1111/poi-net
공식 코드Lite Unified Modeling for Discriminative Reading Comprehension
https://github.com/yilin1111/reknet
공식 코드Reference Knowledgeable Network for Machine Reading Comprehension
https://github.com/yinzhangyue/c2fm
공식 코드https://github.com/yinzhangyue/smoothing-r3
공식 코드https://github.com/ymcui/Chinese-BERT-wwm
공식 코드https://github.com/ymcui/Cross-Lingual-MRC
공식 코드https://github.com/ymcui/ExpMRC
공식 코드ExpMRC: Explainability Evaluation for Machine Reading Comprehension
https://github.com/ymcui/cmrc2017
공식 코드Dataset for the First Evaluation on Chinese Machine Reading Comprehension
https://github.com/ymcui/cmrc2018
공식 코드A Span-Extraction Dataset for Chinese Machine Reading Comprehension
https://github.com/ymcui/cmrc2019
공식 코드A Sentence Cloze Dataset for Chinese Machine Reading Comprehension
https://github.com/ymcui/mrc-model-analysis
공식 코드Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models
https://github.com/yottaxx/et5
공식 코드ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension
https://github.com/youlandasu/act-aware-dst
공식 코드Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State Tracking
https://github.com/yuweihao/reclor
공식 코드ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning
https://github.com/zhiweizhang97/arsjointmodel
공식 코드Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification
https://github.com/zhoucz97/ecpe-mm-r
공식 코드https://github.com/zyz0000/finbert-mrc
공식 코드https://huggingface.co/TownsWu/PEG
공식 코드https://github.com/2023-MindSpore-1/ms-code-218/tree/main/sdne
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/2023-MindSpore-4/Code6/tree/main/SDNet
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/27182812/ChineseBERT_paddle
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
https://github.com/AmenRa/rank_eval
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/BunsenFeng/M-AbstainQA
The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants
https://github.com/FudanNLP/UNet
U-Net: Machine Reading Comprehension with Unanswerable Questions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.