Abstract

Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.

Highlights

  • Retrieval-based dialogue systems conduct conversations by selecting the most appropriate system response given the dialogue history and the input user utterance

  • A comparison to REDDIT-DIRECT further suggests that fine-tuning even with a small amount of in-domain data can lead to large improvements: e.g., the gains over REDDIT-DIRECT are +67.5% on BANKING, +32.5% on UBUNTU, +22.8% on AMAZONQA, and +11.5% on OpenSubtitles dataset (OPENSUB)

  • The results indicate that the synergy between the abundant response (a) ELMO-SIM

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Summary

Introduction

Retrieval-based dialogue systems conduct conversations by selecting the most appropriate system response given the dialogue history and the input user utterance (i.e., the full dialogue context). When framed as an ad-hoc retrieval task (Deerwester et al, 1990; Ji et al, 2014; Kannan et al, 2016; Henderson et al, 2017), the system treats each input utterance as a query and retrieves the most relevant response from a large response collection by computing semantic similarity between the query representation and the encoding of each response in the collection

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