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arXiv · 1606.07056

Emulating Human Conversations using Convolutional Neural Network-based IR

Abhay Prakash·Chris Brockett·Puneet Agrawal
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Abstract

Conversational agents ("bots") are beginning to be widely used in conversational interfaces. To design a system that is capable of emulating human-like interactions, a conversational layer that can serve as a fabric for chat-like interaction with the agent is needed. In this paper, we introduce a model that employs Information Retrieval by utilizing convolutional deep structured semantic neural network-based features in the ranker to present human-like responses in ongoing conversation with a user. In conversations, accounting for context is critical to the retrieval model; we show that our context-sensitive approach using a Convolutional Deep Structured Semantic Model (cDSSM) with character trigrams significantly outperforms several conventional baselines in terms of the relevance of responses retrieved.

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Abhay Prakash, Chris Brockett, Puneet Agrawal. 2016-06-22. Emulating Human Conversations using Convolutional Neural Network-based IR. https://arxiv.org/abs/1606.07056

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