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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/google/mcafp
공식 코드Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors
https://github.com/hanxiao/daanet
공식 코드https://github.com/him1411/edgar10q-dataset
공식 코드https://github.com/howardhsu/RCRC
공식 코드https://github.com/hugochan/GraphFlow
공식 코드https://github.com/hunto/reloss
공식 코드https://github.com/husseinmozannar/SOQAL
공식 코드https://github.com/iflytek/minirbt
공식 코드MiniRBT: A Two-stage Distilled Small Chinese Pre-trained Model
https://github.com/ink-usc/entity-robustness
공식 코드On the Robustness of Reading Comprehension Models to Entity Renaming
https://github.com/jadecxliu/codeqa
공식 코드CodeQA: A Question Answering Dataset for Source Code Comprehension
https://github.com/jd-ai-research-nlp/evidr
공식 코드EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading Comprehension
https://github.com/jd-ai-research-nlp/ror
공식 코드RoR: Read-over-Read for Long Document Machine Reading Comprehension
https://github.com/jind11/MMM-MCQA
공식 코드MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
https://github.com/kevinduh/san_mrc
공식 코드https://github.com/leoleoasd/mlp-based-weighting
공식 코드Contextual embedding and model weighting by fusing domain knowledge on Biomedical Question Answering
https://github.com/lgw863/LogiQA-dataset
공식 코드LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning
https://github.com/lingyongyan/docds
공식 코드https://github.com/liwb1219/hrmf
공식 코드Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained Models
https://github.com/ltgoslo/norquad
공식 코드https://github.com/luciusssss/how-many-answers
공식 코드How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension
https://github.com/luciusssss/why-learn-shortcut
공식 코드https://github.com/luciusssss/xkbqa-as-mrc
공식 코드Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension
https://github.com/lujunru/rolemrc
공식 코드RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following
https://github.com/luka-group/causal-view-of-entity-bias
공식 코드https://github.com/luohongyin/RGX
공식 코드https://github.com/lxucs/multilingual-mrc-isdg
공식 코드Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph
https://github.com/matnlp/cmedmrc
공식 코드https://github.com/mbzuai-nlp/multichoice2extractiveqa
공식 코드From Multiple-Choice to Extractive QA: A Case Study for English and Arabic
https://github.com/microsoft/scglab
공식 코드https://github.com/mmahbub/bioadapt-mrc
공식 코드https://github.com/mosh0110/guiding-llm
공식 코드https://github.com/nerel-ds/nerel-bio
공식 코드NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities
https://github.com/neukg/robustmrc
공식 코드An Understanding-Oriented Robust Machine Reading Comprehension Model
https://github.com/nju-websoft/TSQA
공식 코드https://github.com/nju-websoft/adalogn
공식 코드AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.