تقرير
Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddings
| العنوان: | Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddings |
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| المؤلفون: | Pandey, Gaurav, Contractor, Danish, Joshi, Sachindra |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence |
| الوصف: | 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 (>100). In this paper, we combine the best of both worlds by proposing 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. Comment: 10 pages, 2 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2204.02710 |
| رقم الأكسشن: | edsarx.2204.02710 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |