• DOI:10.18653/v1/2020.emnlp-main.741
Corpus ID: 221749132

The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection

@inproceedings{Lin2020TheWI,
  title={The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection},
  author={Zibo Lin and Deng Cai and Yan Wang and Xiaojiang Liu and Haitao Zheng and Shuming Shi},
  booktitle={EMNLP},
  year={2020}
}

Zibo Lin, Deng Cai, +3 authors Shuming Shi

Published in EMNLP 2020

Computer Science

Response selection plays a vital role in building retrieval-based conversation systems. Despite that response selection is naturally a learning-to-rank problem, most prior works take a point-wise view and train binary classifiers for this task: each response candidate is labeled either relevant (one) or irrelevant (zero). On the one hand, this formalization can be sub-optimal due to its ignorance of the diversity of response quality. On the other hand, annotating grayscale data for learning-to… CONTINUE READING

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