[NLP論文閱讀] A STRUCTURED SELF-ATTENTIVE SENTENCE EMBEDDING
論文原文：A STRUCTURED SELF-ATTENTIVE SENTENCE EMBEDDING 引言 這篇文章提出了使用self-attention技術來生成可解釋句子向量的模型。通常我們使用向量(vector)來表示單詞、短語或者句子向量，在這篇文章中，作者提出可以使用二維句子來表示句子，矩陣的每一行表示句子的不同部分。作者在3個不同的數據集上進行了3種不同的task，分別是author
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相關文章
- 1. 論文引介 | A Structured Self-attentive Sentence Embedding
- 2. A Structured Self-attentive Sentence Embedding 論文筆記
- 3. [ICLR2017]A Structured Self-attentive Sentence Embedding
- 4. [NLP論文閱讀]A SIMPLE BUT TOUGH-TO-BEAT BASELINE FOR SENTENCE EMBEDDINGS
- 5. Sentence-BERT 論文閱讀
- 6. 讀書筆記16：A structured self-attentive sentence embedding（ICLR 2017）
- 7. [NLP論文閱讀] Supervised Learning of Universal Sentence Representations from NLI data
- 8. 論文閱讀 | Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
- 9. 論文閱讀理解 - Deep Metric Learning via Lifted Structured Feature Embedding
- 10. 論文閱讀：Deep Metric Learning via Lifted Structured Feature Embedding