Papers with Response selection

2 papers
Constructing Interpretive Spatio-Temporal Features for Multi-Turn Responses Selection (P19-1)

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Challenge: Existing models for response selection do not perform well when there are many candidate responses.
Approach: They propose a Spatio-Temporal Matching network (STM) for response selection . they use soft alignment to obtain local relevance between context and response .
Outcome: The proposed model significantly outperforms the state-of-the-art model on two large-scale multi-turn response selection tasks.
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection (2020.emnlp-main)

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Challenge: Existing approaches to learning-to-rank response selection are suboptimal due to ignorance of diversity of response quality.
Approach: They propose to use off-the-shelf response retrieval models as automatic grayscale data generators to train response selection models.
Outcome: The proposed approach can be automated without human effort on grayscale data.

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