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
相關文章
- 1. 論文閱讀《Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks》
- 2. 論文閱讀：Associative Embedding: End-to-End Learning for Joint Detection and Grouping
- 3. 【論文閱讀】Distributed Representation of Sentence and Document
- 4. 論文閱讀——Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 5. 論文閱讀之：Associatively Segmenting Instances and Semantics in Point Clouds
- 6. 1604.Joint Detection and Identification Feature Learning for Person Search論文閱讀筆記
- 7. 『 論文閱讀』Slot-Gated Modeling for Joint Slot Filling and Intent Prediction
- 8. 『 論文閱讀』Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
- 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》
- 更多相關文章...
- • W3C RDF and OWL 活動 - W3C 教程
- • XSL-FO table-and-caption 對象 - XSL-FO 教程
- • RxJava操作符（七）Conditional and Boolean
- • JDK13 GA發佈：5大特性解讀
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. 論文閱讀《Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks》
- 2. 論文閱讀：Associative Embedding: End-to-End Learning for Joint Detection and Grouping
- 3. 【論文閱讀】Distributed Representation of Sentence and Document
- 4. 論文閱讀——Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 5. 論文閱讀之：Associatively Segmenting Instances and Semantics in Point Clouds
- 6. 1604.Joint Detection and Identification Feature Learning for Person Search論文閱讀筆記
- 7. 『 論文閱讀』Slot-Gated Modeling for Joint Slot Filling and Intent Prediction
- 8. 『 論文閱讀』Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
- 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》