Abstract
Embedding-based approaches for dialog response retrieval embed the context-response pairs as points in the embedding space. These approaches are scalable, but fail to account for the complex, many-to-many relationships that exist between context-response pairs. On the other end of the spectrum, there are approaches that feed the context-response pairs jointly through multiple layers of neural networks. These approaches can model the complex relationships between context-response pairs, but fail to scale when the set of responses is moderately large (>1000). In this paper, we propose a scalable model that can learn complex relationships between context-response pairs. Specifically, the model maps the contexts as well as responses to probability distributions over the embedding space. We train the models by optimizing the Kullback-Leibler divergence between the distributions induced by context-response pairs in the training data. We show that the resultant model achieves better performance as compared to other embedding-based approaches on publicly available conversation data.- Anthology ID:
- 2022.findings-emnlp.239
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3273–3287
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.239
- DOI:
- 10.18653/v1/2022.findings-emnlp.239
- Cite (ACL):
- Gaurav Pandey, Danish Contractor, and Sachindra Joshi. 2022. Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddings. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3273–3287, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddings (Pandey et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.findings-emnlp.239.pdf