Reading Comprehension
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 20
- 평가표
- 7
- 보존된 평가 기록
- 91
Most current question answering datasets frame the task as reading comprehension where the question is about a paragraph or document and the answer often is a span in the document.
Some specific tasks of reading comprehension include multi-modal machine reading comprehension and textual machine reading comprehension, among others. In the literature, machine reading comprehension can be divide into four categories: cloze style, multiple choice, span prediction, and free-form answer. Read more about each category here.
Benchmark datasets used for testing a model's reading comprehension abilities include MovieQA, ReCoRD, and RACE, among others.
The Machine Reading group at UCL also provides an overview of reading comprehension tasks.
Figure source: A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/samuelarnesen/nyu-debate-modeling
공식 코드Training Language Models to Win Debates with Self-Play Improves Judge Accuracy
https://github.com/saptarshi059/cdqa-project
공식 코드Towards Efficient Methods in Medical Question Answering using Knowledge Graph Embeddings
https://github.com/saurabhhssaurabh/reviews_summarization
공식 코드MRCBert: A Machine Reading ComprehensionApproach for Unsupervised Summarization
https://github.com/schlevik/dataset-analysis
공식 코드A Framework for Evaluation of Machine Reading Comprehension Gold Standards
https://github.com/schlevik/sam
공식 코드Semantics Altering Modifications for Evaluating Comprehension in Machine Reading
https://github.com/sebastianarnold/SECTOR
공식 코드SECTOR: A Neural Model for Coherent Topic Segmentation and Classification
https://github.com/sebastianarnold/WikiSection
공식 코드SECTOR: A Neural Model for Coherent Topic Segmentation and Classification
https://github.com/shannonai/mrc-srl
공식 코드https://github.com/shashank140195/Biomedical_QA_Model
공식 코드Top K Relevant Passage Retrieval for Biomedical Question Answering
https://github.com/shibshib/Moe-VRD
공식 코드https://github.com/shimisalant/CWR
공식 코드Contextualized Word Representations for Reading Comprehension
https://github.com/shmsw25/DecompRC
공식 코드Multi-hop Reading Comprehension through Question Decomposition and Rescoring
https://github.com/shmsw25/single-hop-rc
공식 코드Compositional Questions Do Not Necessitate Multi-hop Reasoning
https://github.com/shonosuke/ishiwatari-naacl2019
공식 코드https://github.com/shuohangwang/SeqMatchSeq
공식 코드https://github.com/shuohangwang/comatch
공식 코드https://github.com/shuohangwang/mprc
공식 코드R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering
https://github.com/shuzou-1/simlabel
공식 코드SimLabel: Consistency-Guided OOD Detection with Pretrained Vision-Language Models
https://github.com/siangooding/readability_scroll
공식 코드https://github.com/sjtu-compling/llm-pragmatics
공식 코드Do Large Language Models Understand Conversational Implicature -- A case study with a chinese sitcom
https://github.com/sogou/SMRCToolkit
공식 코드https://github.com/sohamparikh94/ElimiNet
공식 코드ElimiNet: A Model for Eliminating Options for Reading Comprehension with Multiple Choice Questions
https://github.com/som-shahlab/ehr-rwe
공식 코드https://github.com/somiltg/bert
공식 코드BERT Based Multilingual Machine Comprehension in English and Hindi
https://github.com/songxiaoshuai/CGID
공식 코드Continual Generalized Intent Discovery: Marching Towards Dynamic and Open-world Intent Recognition
https://github.com/sonlam1102/vicoqa-cmc
공식 코드Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts
https://github.com/sonlam1102/vimmrc2
공식 코드A Multiple Choices Reading Comprehension Corpus for Vietnamese Language Education
https://github.com/sonlam1102/vlogqa
공식 코드VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension
https://github.com/sonqt/single-sentence-reader
공식 코드Single-Sentence Reader: A Novel Approach for Addressing Answer Position Bias
https://github.com/sparkjiao/merit
공식 코드MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning
https://github.com/stonybrooknlp/dire
공식 코드Is Multihop QA in DiRe Condition? Measuring and Reducing Disconnected Reasoning
https://github.com/stonybrooknlp/suqa
공식 코드Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension
https://github.com/stoyian/OCaTS
공식 코드https://github.com/strong-ai-lab/logical-and-abstract-reasoning
공식 코드https://github.com/strong-ai-lab/logical-equivalence-driven-amr-data-augmentation-for-representation-learning
공식 코드Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.