Related papers: Joint Learning of Context and Feedback Embeddings in Spoken Dialogue

Joint Learning of Context and Feedback Embeddings in Spoken Dialogue

URL: http://arxiv.org/abs/2406.07291v1
Date: Tue, 11 Jun 2024 14:22:37 GMT
Title: Joint Learning of Context and Feedback Embeddings in Spoken Dialogue
Authors: Livia Qian, Gabriel Skantze,
Abstract summary: We investigate the possibility of embedding short dialogue contexts and feedback responses in the same representation space using a contrastive learning objective. Our results show that the model outperforms humans given the same ranking task and that the learned embeddings carry information about the conversational function of feedback responses.
Score: 3.8673630752805446
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Short feedback responses, such as backchannels, play an important role in spoken dialogue. So far, most of the modeling of feedback responses has focused on their timing, often neglecting how their lexical and prosodic form influence their contextual appropriateness and conversational function. In this paper, we investigate the possibility of embedding short dialogue contexts and feedback responses in the same representation space using a contrastive learning objective. In our evaluation, we primarily focus on how such embeddings can be used as a context-feedback appropriateness metric and thus for feedback response ranking in U.S. English dialogues. Our results show that the model outperforms humans given the same ranking task and that the learned embeddings carry information about the conversational function of feedback responses.

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