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.
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sunzeyeah/rlhf
https://github.com/surancy/nlp-classification-with-BERT
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https://github.com/surekhamedapati/NLPA_NEO
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https://github.com/svakulenk0/response_eval
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/switiz/las.pytorch
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https://github.com/tatsu-lab/stanford_alpaca
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https://github.com/tdd-ai/data-collection
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https://github.com/techit-limtiyakul/bidirectional-attention-flow
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tensorflow/models/tree/master/official/nlp
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tensorflow/models/tree/master/official/nlp/albert
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/theQuert/inlpfun
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https://github.com/thecodemasterk/Text-to-Text-transfer-transformers
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https://github.com/thinkwee/DPP_CNN_Summarization
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https://github.com/tighu20/Kaggle-Tweet-Sentiment-Extraction
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출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.