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/TheNeuromancer/SentEmb
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/TidalPaladin/neural-summarizer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/TonyX19/bert_hyperpartisan
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Unbabel/COMET
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/UnknownGenie/altered-BERT-KPE
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/VachanVY/gpt.jax
https://github.com/Vladimetr/ASR-Knowledge-Transferring
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/W4ngatang/qags
https://github.com/Wadaboa/squad-question-answering
https://github.com/Walter-B/bert-20-classes
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/WarruzuEndo/BiDAF_mindspore
https://github.com/WindQAQ/listen-attend-and-spell
https://github.com/X-LANCE/WebSRC-Baseline
WebSRC: A Dataset for Web-Based Structural Reading Comprehension
https://github.com/XINZXZHANG/Bert_repo
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Xaniar87/SAN_SQuAD2
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/Xinsen-Zhang/transformer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Y1ran/NLP-BERT--ChineseVersion
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/YYGXjpg/BERT_WL
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Yifan-Gao/Distractor-Generation-RACE
Generating Distractors for Reading Comprehension Questions from Real Examinations
https://github.com/Yipeng91/text_classifier_pub
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/YongWookHa/BERT-on-Pytorch-Template
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/YuanEric88/bert-nlu
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/YuehChuan/bert-Agus
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ZHEvent/ZHEvent.github.io
ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning
https://github.com/ZHO9504/Select-Key-Terms-in-a-Question
https://github.com/Zeeshan75/Bert_Telugu_Ner
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Zehui127/SQUAD_BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ZhangShiyue/QGforQA
https://github.com/a414351664/Bert_MRC
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/a414351664/NarrativeQA
Commonsense for Generative Multi-Hop Question Answering Tasks
https://github.com/aaronHuang0121/AI-CUP-2020
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aaronbae/AnaQA
https://github.com/aartika/experiment1
https://github.com/abdumaa/hiqualprop
https://github.com/abelriboulot/onnxt5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.