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/beyondguo/genius
공식 코드https://github.com/bigdata-ustc/educat
공식 코드Survey of Computerized Adaptive Testing: A Machine Learning Perspective
https://github.com/bimk/intelligent-education
공식 코드Diffusion-Inspired Cold Start with Sufficient Prior in Computerized Adaptive Testing
https://github.com/boyuanzheng010/SemEval2021-Reading-Comprehension-of-Abstract-Meaning
공식 코드SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning
https://github.com/bright2013/CorefAwareMRC
공식 코드Tracing Origins: Coreference-aware Machine Reading Comprehension
https://github.com/brmson/dataset-sts
공식 코드Sentence Pair Scoring: Towards Unified Framework for Text Comprehension
https://github.com/bytedance/MTVQA
공식 코드MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering
https://github.com/bytedance/textharmony
공식 코드https://github.com/callsys/textvr
공식 코드A Large Cross-Modal Video Retrieval Dataset with Reading Comprehension
https://github.com/caoyu1991/CASe
공식 코드https://github.com/cather-chen/logical-reasoning-graph
공식 코드https://github.com/chaitanyamalaviya/annotator-heuristics
공식 코드Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and Models
https://github.com/chaitanyamalaviya/pachinko
공식 코드https://github.com/chaitanyamalaviya/rationale_formats
공식 코드https://github.com/chen-gx/mprompt
공식 코드MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension
https://github.com/chiahsuan156/ODSQA
공식 코드https://github.com/chiahsuan156/Spoken-SQuAD
공식 코드https://github.com/chujiezheng/ChID-Dataset
공식 코드https://github.com/cisnlp/mexa
공식 코드MEXA: Multilingual Evaluation of English-Centric LLMs via Cross-Lingual Alignment
https://github.com/clairebarale/probing_legal_entity_types
공식 코드Do Language Models Learn about Legal Entity Types during Pretraining?
https://github.com/clips/clicr
공식 코드CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehension
https://github.com/cooelf/AwesomeMRC
공식 코드https://github.com/cooelf/SemBERT
공식 코드https://github.com/cooelf/instancereg
공식 코드Instance Regularization for Discriminative Language Model Pre-training
https://github.com/cooelf/subMrc
공식 코드https://github.com/cooelf/subword_seg
공식 코드https://github.com/copenlu/spanex
공식 코드https://github.com/cos4007/grape_demonstrations
공식 코드https://github.com/crossrm/gsls
공식 코드https://github.com/csitfun/logieval
공식 코드Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4
https://github.com/cvdigitalai/katecheo
공식 코드Katecheo: A Portable and Modular System for Multi-Topic Question Answering
https://github.com/damithdr/questionanswering
공식 코드https://github.com/damo-nlp-sg/pmr
공식 코드https://github.com/dandelin/vilt
공식 코드ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
https://github.com/danqi/rc-cnn-dailymail
공식 코드A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.