Summary of Joint Learning Of Context and Feedback Embeddings in Spoken Dialogue, by Livia Qian and Gabriel Skantze
Joint Learning of Context and Feedback Embeddings in Spoken Dialogue
by Livia Qian, Gabriel Skantze
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the role of short feedback responses, like backchannels, in spoken dialogue, focusing on how their lexical and prosodic form influence their appropriateness and conversational function. The authors investigate using a contrastive learning objective to embed short dialogue contexts and feedback responses in the same representation space, evaluating its potential as a context-feedback appropriateness metric for ranking feedback responses in U.S. English dialogues. The results show that the model outperforms humans in the ranking task and that the learned embeddings capture information about the conversational function of feedback responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is all about understanding how we give and receive feedback in conversations. It’s like when you’re talking to a friend and you say “uh-huh” or “yeah” to show you’re listening. The researchers are trying to figure out how these little comments, called backchannels, work and how they can be used to make conversations better. They came up with a new way to use computers to understand when feedback is helpful and when it’s not. This could help machines have more natural conversations with humans. |
