論文閱讀：Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG
簡介： 基於神經網絡的自然語言生成(NNLG)領域進行文本生成時經常有兩大瓶頸，（1）缺乏大量(meaning representation)MR to (natural language)NL的數據；（2）不能系統的控制輸出的重要方面，從而使得生成不同風格的文本；作者這篇文章圍繞這兩個問題，基於Yelp數據集(代表能夠容易獲取的描述性的數據集)，能生成出具有不同標籤同時大量的和原文一樣
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相關文章
- 1. 論文閱讀《Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks》
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- 9. Event StoryLine Corpus 論文閱讀
- 10. 論文閱讀《Performance Comparison of Neural and Non-Neural Approaches to Session-based Recommendation》