Improving Matching Models with Hierarchical Contextualized Representations for Multi-turn Response Selection

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Abstract

In this paper, we study context-response matching with pre-trained contextualized representations for multi-turn response selection in retrieval-based chatbots. Existing models, such as Cove and ELMo, are trained with limited context (often a single sentence or paragraph), and may not work well on multi-turn conversations, due to the hierarchical nature, informal language, and domain-specific words. To address the challenges, we propose pre-training hierarchical contextualized representations, including contextual word-level and sentence-level representations, by learning a dialogue generation model from large-scale conversations with a hierarchical encoder-decoder architecture. Then the two levels of representations are blended into the input and output layer of a matching model respectively. Experimental results on two benchmark conversation datasets indicate that the proposed hierarchical contextualized representations can bring significantly and consistently improvement to existing matching models for response selection.

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Tao, C., Wu, W., Feng, Y., Zhao, D., & Yan, R. (2020). Improving Matching Models with Hierarchical Contextualized Representations for Multi-turn Response Selection. In SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1865–1868). Association for Computing Machinery, Inc. https://doi.org/10.1145/3397271.3401290

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