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Response Selection for End-to-End Retrieval-Based Dialogue Systems

Abstract : The increasing need of human assistance pushed researchers to develop automatic, smart and tireless dialogue systems that can converse with humans in natural language to be either their virtual assistant or their chat companion. The industry of dialogue systems has been very popular in the last decade and many systems from industry and academia have been developed. In this thesis, we study retrieval-based dialogue systems which aim to find the most appropriate response to the conversation among a set of predefined responses. The main challenge of these systems is to understand the conversation and identify the elements that describe the problem and the solution which are usually implicit. Most of the recent approaches are based on deep learning techniques which can automatically capture implicit information. However these approaches are either complex or domain dependent. We propose a simple, end-to-end and efficient retrieval-based dialogue system that first matches the response with the history of the conversation on the sequence-level and then we extend the system to multiple levels while keeping the architecture simple and domain independent. We perform several analyzes to determine possible improvements.
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Submitted on : Monday, August 31, 2020 - 8:27:42 PM
Last modification on : Friday, August 5, 2022 - 2:54:51 PM
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  • HAL Id : tel-02926608, version 1


Basma El Amel Boussaha. Response Selection for End-to-End Retrieval-Based Dialogue Systems. Computation and Language [cs.CL]. Université de Nantes (UN), 2019. English. ⟨tel-02926608⟩



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