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/HSabbar/Question-Answering
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/Hibb-bb/ALLM
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Holldean/BERT-Pruning
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/HuihuiChyan/BJTUNLP_Practice2020
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/HuihuiChyan/BJTUNLP_Practice2021
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/IBM/MAX-Question-Answering
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/IBM/MAX-Text-Sentiment-Classifier
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/IBM/MAX-Toxic-Comment-Classifier
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/IMPLabUniPr/UniParma-at-semeval-2021-task-5
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Impavidity/relogic
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/IndicoDataSolutions/finetune
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/JA-Bar/nlp-depression
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/JJoving/SMLAT
https://github.com/JMSaindon/NlpSquad
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/JNUpython/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/JONGWE1/Paddle-DeBERTa
https://github.com/JRC1995/Abstractive-Summarization
https://github.com/Jackma3/NER-Bert-BiLstm
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Jason-J-Choi/DeBERTa_TxtClassifier
https://github.com/JeheonPark596/sentimentsurvival
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/JoegameZhou/mPanGu-Alpha-53
https://github.com/JohannLee1996/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Jun-Zhang-32108/Sentiment-Analysis
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/JunnYu/x-transformers-paddle
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/KAGUYAHONGLAI/SRC
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/KLUE-benchmark/KLUE-baseline
https://github.com/Kanishkparganiha/Named-Entity-Recognition-using-BERT-with-PyTorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Karine-Huang/T2I-CompBench
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
https://github.com/Karthik-Bhaskar/Context-Based-Question-Answering
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Katherinaxxx/Paper-ReadingNotes
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual Transfer
https://github.com/Katsumata420/bert_mlm
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/KeithGalli/pycon2020
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Kerram/Deephol-Bert-Zpp
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Kevin-Vora/bert-embedding-gluonnlp-edit-
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Ki6an/fastT5
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.