End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension
來源 arXiv 2016.10.31 問題 當前的 RC 模型都是生成單個實體或者單個詞，不能夠根據問題動態生成答案。基於此，本文提出了 end2end 的 chunk 抽取神經網絡。 文章思路 Dynamic Chunk Reader 這一模型分成四步： encode layer 分別使用 bi-GRU 對 passage 和 question 進行編碼，這裏面的每個詞的表示是由 word e
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相關文章
- 1. 論文筆記--From Answer Extraction to Answer Generation for Machine Reading Comprehension (S-Net)
- 2. Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension 論文閱讀筆記
- 3. Reading Note: Gated Self-Matching Networks for Reading Comprehension and Question Answering
- 4. Simple and Effective Multi-Paragraph Reading Comprehension
- 5. MACHINE COMPREHENSION USING MATCH-LSTM AND ANSWER POINTER(MATCH-LSTM)
- 6. Attention-over-Attention Neural Networks for Reading Comprehension 訊飛
- 7. 【論文翻譯+筆記】Neural Machine Reading Comprehension: Methods and Trends
- 8. machine comprehension using match-lstm and answer pointer
- 9. [ICLR2017] Machine Comprehension Using Match-LSTM and Answer Pointer
- 10. A Deep Cascade Model for Multi-Document Reading Comprehension瀑布模型 閱讀理解筆記