論文筆記--From Answer Extraction to Answer Generation for Machine Reading Comprehension (S-Net)
First published on indexfziq.github.io at 2019-01-17 19:10:40. Introduction 本文出自微軟亞研院，一作是北航的Chuanqi Tan, 代碼沒有公開，鏈接爲臺灣的一位同學用CNTK實現的代碼。首次使用了Seq2Seq模型對MS-MARCO閱讀理解數據集進行答案生成，比較符合MS-MARCO的本意，同時定義了先抽取再生成的框架
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相關文章
- 1. 論文筆記--Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification (V-Net)
- 2. End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension
- 3. Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension 論文閱讀筆記
- 4. 【論文翻譯+筆記】Neural Machine Reading Comprehension: Methods and Trends
- 5. 論文《Adversarial Reading Networks For Machine Comprehension》
- 6. 【論文筆記03】ReasoNet: Learning to Stop Reading in Machine Comprehension
- 7. MACHINE COMPREHENSION USING MATCH-LSTM AND ANSWER POINTER(MATCH-LSTM)
- 8. V-Net 《Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification》閱讀理解筆記
- 9. machine comprehension using match-lstm and answer pointer
- 10. [ICLR2017] Machine Comprehension Using Match-LSTM and Answer Pointer