论文标题
混合匹配：可扩展的对话框响应响应检索使用高斯混合物嵌入
Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddings
论文作者
论文摘要
对话框响应检索基于嵌入的方法将上下文响应对作为嵌入空间中的点。这些方法是可扩展的，但无法说明上下文响应对之间存在的复杂，多一的关系。在频谱的另一端，有一些方法可以通过多层神经网络共同供应上下文响应对。这些方法可以建模上下文响应对之间的复杂关系，但是当响应集中较大（> 100）时，无法扩展。在本文中，我们通过提出一个可扩展的模型来结合两全其美的最佳模型，该模型可以学习上下文响应对之间的复杂关系。具体而言，模型映射上下文以及对嵌入空间上概率分布的响应。我们通过优化训练数据中上下文响应对引起的分布之间的kullback-leibler差异来训练模型。我们表明，与公开可用的对话数据中的其他基于嵌入式的方法相比，最终的模型可以取得更好的性能。
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.
