Papers › The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection
Zibo Lin, Deng Cai, Yan Wang, Xiaojiang Liu, Hai-Tao Zheng, Shuming Shi
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-rank can be prohibitively expensive and challenging. In this work, we show that grayscale data can be automatically constructed without human effort. Our method employs off-the-shelf response retrieval models and response generation models as automatic grayscale data generators. With the constructed grayscale data, we propose multi-level ranking objectives for training, which can (1) teach a matching model to capture more fine-grained context-response relevance difference and (2) reduce the train-test discrepancy in terms of distractor strength. Our method is simple, effective, and universal. Experiments on three benchmark datasets and four state-of-the-art matching models show that the proposed approach brings significant and consistent performance improvements.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Conversational Response Selection | E-commerce | G-MSN | R10@1 | 0.613 | #11 of 15 | Archive leaderboard | report |
| Conversational Response Selection | E-commerce | G-MSN | R10@2 | 0.786 | #11 of 15 | Archive leaderboard | report |
| Conversational Response Selection | E-commerce | G-MSN | R10@5 | 0.964 | #11 of 15 | Archive leaderboard | report |
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