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://bitbucket.org/TomKenter/attentive-memory-networks-code
공식 코드Attentive Memory Networks: Efficient Machine Reading for Conversational Search
https://github.com/1094701018/ketm
공식 코드https://github.com/21644h/DP_QA_joint_model
공식 코드https://github.com/8023looker/datasculpt
공식 코드DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective Partitioning
https://github.com/ACL2020Anonymous/SymBERT
공식 코드Symmetric Regularization based BERT for Pair-wise Semantic Reasoning
https://github.com/AkariAsai/extractive_rc_by_runtime_mt
공식 코드Multilingual Extractive Reading Comprehension by Runtime Machine Translation
https://github.com/AkariAsai/logic_guided_qa
공식 코드Logic-Guided Data Augmentation and Regularization for Consistent Question Answering
https://github.com/Alab-NII/mrc-heuristics
공식 코드https://github.com/AlanSwift/DH-VQG
공식 코드https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/pace
공식 코드PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts
https://github.com/Anni-Zou/DocBench
공식 코드DOCBENCH: A Benchmark for Evaluating LLM-based Document Reading Systems
https://github.com/AshishMittal/RMLIntent
공식 코드Representation based meta-learning for few-shot spoken intent recognition
https://github.com/BananaTonic/Material_Synthesis_Corpus
공식 코드Analyzing Research Trends in Inorganic Materials Literature Using NLP
https://github.com/BinWang28/InstructDS
공식 코드https://github.com/CLARIN-PL/PUGG
공식 코드Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction
https://github.com/CLUEbenchmark/CLUE
공식 코드https://github.com/CLUEbenchmark/FewCLUE
공식 코드https://github.com/Chandrahasd/OKGIT
공식 코드OKGIT: Open Knowledge Graph Link Prediction with Implicit Types
https://github.com/CongSun-dlut/BioBERT-MRC
공식 코드Biomedical named entity recognition using BERT in the machine reading comprehension framework
https://github.com/DFKI-NLP/CoXQL
공식 코드CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI Systems
https://github.com/DancingSoul/NQ_BERT-DM
공식 코드Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension
https://github.com/DigitalPhonetics/reading-comprehension
공식 코드https://github.com/Disiok/poetry-seq2seq
공식 코드Hierarchical Attention: What Really Counts in Various NLP Tasks
https://github.com/Dmmm1997/DeRIS
공식 코드https://github.com/Eleanor-H/DAGN
공식 코드https://github.com/Eric-Wallace/universal-triggers
공식 코드Universal Adversarial Triggers for Attacking and Analyzing NLP
https://github.com/Evan-Gao/Distractor-Generation-RACE
공식 코드Generating Distractors for Reading Comprehension Questions from Real Examinations
https://github.com/Francois-Aubet/EQuANt
공식 코드https://github.com/GeekDream-x/IDOL
공식 코드IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning
https://github.com/GuillermoPuebla/RelationReasonNN
공식 코드https://github.com/HIT-SCIR/Molweni
공식 코드https://github.com/HLR/LatentAlignmentProcedural
공식 코드Latent Alignment of Procedural Concepts in Multimodal Recipes
https://github.com/Heidelberg-NLP/discourse-aware-semantic-self-attention
공식 코드Discourse-Aware Semantic Self-Attention for Narrative Reading Comprehension
https://github.com/HongyuGong/RCM-Question-Answering
공식 코드Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension
https://github.com/Huntersxsx/MGPN
공식 코드You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.