論文閱讀：RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems
核心問題：What makes a good reply in open-domain dialog systems? 一、Observation 1、Resembling the groundtruth generally implies a good reply. 生成的reply和groundtruth相似度越高越好。這是一個general assumption。 我們需要注意：short-
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相關文章
- 1. An Empirical Evaluation of In-Memory Multi-Version Concurrency Control 論文閱讀筆記
- 2. How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for
- 3. 論文閱讀：《Wide & Deep Learning for Recommender Systems》
- 4. 論文閱讀：Automatic Generation of Data-Oriented Exploits
- 5. 論文閱讀筆記：Recent Advances and Challenges in Task-oriented Dialog Systems
- 6. Unsupervised Learning of Edges 論文閱讀
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