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/graykode/ALBERT-Pytorch
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/graykode/nlp-tutorial
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/graykode/xlnet-Pytorch
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/greenbitai/low_bit_llama
https://github.com/guilk/vlc
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
https://github.com/guinmoon/llmfarm
https://github.com/gulucaptain/dynamictrl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/guoyaohua/BERT-Chinese-Annotation
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/guoyaohua/BERT-Classifier
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/guzhang480/Google_BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/h4ste/oscar
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/habibullah-araphat/BERT-GPU
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/habibullah-araphat/BERT-NER-TPU
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/haidershaour/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/haisongzhang/roberta-tiny-cased
https://github.com/haiyang-w/git
https://github.com/halo090770/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/hamishivi/easylm
https://github.com/han-shi/SparseBERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/happen2me/complex-web-questions-dataset
https://github.com/harenlin/IMDB-Sentiment-Analysis-Using-BERT-Fine-Tuning
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/hate-alert/indicabusive
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/haydlite/sparse-bert-ner
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/hazyresearch/ama_prompting
https://github.com/hegebharat/SelfDiscovery
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/hegebharat/sentiment-Analysis-for-German-Datasets
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/helboukkouri/character-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/heraclex12/vietpunc
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/hgstudent/las
https://github.com/hieudepchai/BERT_IE
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/hieunghia-pat/ViConsFormer
https://github.com/hilberthit/gpt-3
https://github.com/hit-scir/huozi
https://github.com/hitvoice/DrQA
https://github.com/hkuds/easyrec
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.