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arXiv · 2004.11045

Distilling Knowledge for Fast Retrieval-based Chat-bots

Amir Vakili Tahami·Kamyar Ghajar·Azadeh Shakery
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Abstract

Response retrieval is a subset of neural ranking in which a model selects a suitable response from a set of candidates given a conversation history. Retrieval-based chat-bots are typically employed in information seeking conversational systems such as customer support agents. In order to make pairwise comparisons between a conversation history and a candidate response, two approaches are common: cross-encoders performing full self-attention over the pair and bi-encoders encoding the pair separately. The former gives better prediction quality but is too slow for practical use. In this paper, we propose a new cross-encoder architecture and transfer knowledge from this model to a bi-encoder model using distillation. This effectively boosts bi-encoder performance at no cost during inference time. We perform a detailed analysis of this approach on three response retrieval datasets.

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Amir Vakili Tahami, Kamyar Ghajar, Azadeh Shakery. 2020-04-23. Distilling Knowledge for Fast Retrieval-based Chat-bots. https://arxiv.org/abs/2004.11045

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