Reading Comprehension
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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/datascienceuibk/arabicaqa
공식 코드ArabicaQA: A Comprehensive Dataset for Arabic Question Answering
https://github.com/datascienceuibk/chroniclingamericaqa
공식 코드https://github.com/davidgolub/QuestionGeneration
공식 코드Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
https://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/deep-spin/treqa
공식 코드Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question Answering
https://github.com/deepmind/rc-data
공식 코드https://github.com/derekmma/dialogue-discourse-relation
공식 코드Implicit Discourse Relation Identification for Open-domain Dialogues
https://github.com/dfki-nlp/llmcheckup
공식 코드https://github.com/dili-lab/potec
공식 코드PoTeC: A German Naturalistic Eye-tracking-while-reading Corpus
https://github.com/dkalpakchi/embrace
공식 코드https://github.com/dkalpakchi/quinductor
공식 코드https://github.com/dkalpakchi/swe_quinductor
공식 코드Automatically generating question-answer pairs for assessing basic reading comprehension in Swedish
https://github.com/dkalpakchi/swequad-mc
공식 코드https://github.com/dpappas/Data-Augmentation-for-Biomedical-Factoid-Question-Answering
공식 코드https://github.com/duorc/duorc
공식 코드DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension
https://github.com/dxlong2000/cohs-cqg
공식 코드CoHS-CQG: Context and History Selection for Conversational Question Generation
https://github.com/easonnie/semanticRetrievalMRS
공식 코드Revealing the Importance of Semantic Retrieval for Machine Reading at Scale
https://github.com/ecnu-text-computing/dcs
공식 코드https://github.com/eladsegal/tag-based-multi-span-extraction
공식 코드A Simple and Effective Model for Answering Multi-span Questions
https://github.com/elronbandel/lexical-generalization
공식 코드Lexical Generalization Improves with Larger Models and Longer Training
https://github.com/epfllm/megatron-llm
공식 코드https://github.com/eric-ai-lab/minigpt-5
공식 코드MiniGPT-5: Interleaved Vision-and-Language Generation via Generative Vokens
https://github.com/eric0052/llm4dr
공식 코드Using LLMs in Generating Design Rationale for Software Architecture Decisions
https://github.com/ericchamoun/FocusedFeedbackGeneration
공식 코드Automated Focused Feedback Generation for Scientific Writing Assistance
https://github.com/ericlee8/multi-party-dialogue-mrc
공식 코드https://github.com/esdurmus/summary-faithfulness
공식 코드https://github.com/eth-lre/engage-your-readers
공식 코드How to Engage Your Readers? Generating Guiding Questions to Promote Active Reading
https://github.com/eth-lre/solving-biases
공식 코드Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?
https://github.com/expertailab/spaceqa
공식 코드SpaceQA: Answering Questions about the Design of Space Missions and Space Craft Concepts
https://github.com/exploration-lab/comumdr
공식 코드CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversations
https://github.com/facebook/bAbI-tasks
공식 코드Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/facebookresearch/DrQA
공식 코드https://github.com/facebookresearch/belebele
공식 코드The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants
https://github.com/facebookresearch/daqa
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.