Domain Aware Neural Dialog System

Abstract

We investigate the task of building a domain aware chat system which generates intelligent responses in a conversation comprising of different domains. The domain, in this case, is the topic or theme of the conversation. To achieve this, we present DOM-Seq2Seq, a domain aware neural network model based on the novel technique of using domain-targeted sequence-to-sequence models (Sutskever et al., 2014) and a domain classifier. The model captures features from current utterance and domains of the previous utterances to facilitate the formation of relevant responses. We evaluate our model on automatic metrics and compare our performance with the Seq2Seq model.

References in corpus (2)

• How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
• Topic Aware Neural Response Generation

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• Correction of Faulty Background Knowledge based on Condition Aware and Revise Transformer for Question Answering
• AI-Powered Text Generation for Harmonious Human-Machine Interaction: Current State and Future Directions