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/momohuang/FlowQA
공식 코드FlowQA: Grasping Flow in History for Conversational Machine Comprehension
https://github.com/momohuang/FusionNet-NLI
공식 코드FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
https://github.com/mosh0110/guiding-llm
공식 코드https://github.com/mrqa/MRQA-Shared-Task-2019
공식 코드MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension
https://github.com/neelabhsinha/qa-prompting
공식 코드QA-prompting: Improving Summarization with Large Language Models using Question-Answering
https://github.com/neilfei/brivl-nmi
공식 코드Towards artificial general intelligence via a multimodal foundation model
https://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/neurasearch/neurips-2022-submission-3358
공식 코드ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler
https://github.com/ni9elf/automated-scoring
공식 코드Automated Scoring for Reading Comprehension via In-context BERT Tuning
https://github.com/nii-cl/qa-text-source-comparison
공식 코드https://github.com/nijianmo/arc-etrr-code
공식 코드https://github.com/nju-websoft/Remp
공식 코드Crowdsourced Collective Entity Resolution with Relational Match Propagation
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
https://github.com/njunlp/SKER
공식 코드Synonym Knowledge Enhanced Reader for Chinese Idiom Reading Comprehension
https://github.com/nlp-anonymous-happy/anonymous-kg-guided-nlp
공식 코드https://github.com/nlp-rl/mmcsg
공식 코드https://github.com/nlpdata/evidence
공식 코드Evidence Sentence Extraction for Machine Reading Comprehension
https://github.com/nlpdata/strategy
공식 코드Improving Machine Reading Comprehension with General Reading Strategies
https://github.com/nolanoorg/spectrasuite
공식 코드Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale
https://github.com/nttmdlab-nlp/VisualMRC
공식 코드https://github.com/nuochenpku/orca
공식 코드Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension
https://github.com/nuochenpku/penguin
공식 코드Natural Response Generation for Chinese Reading Comprehension
https://github.com/nusnlp/amanda
공식 코드A Question-Focused Multi-Factor Attention Network for Question Answering
https://github.com/nyu-dl/SearchQA
공식 코드SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
https://github.com/nyu-mll/CNLI-generalization
공식 코드https://github.com/ocastel/exact-extract
공식 코드How Optimal is Greedy Decoding for Extractive Question Answering?
https://github.com/ofa-sys/ditto
공식 코드https://github.com/omossad/fat-albert
공식 코드FAT ALBERT: Finding Answers in Large Texts using Semantic Similarity Attention Layer based on BERT
https://github.com/openai/gpt-3
공식 코드https://github.com/opennlg/openba
공식 코드OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch
https://github.com/oriyor/turning_tables
공식 코드https://github.com/owenzx/InstabilityAnalysis
공식 코드The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions
https://github.com/ozyyshr/DGM
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.