A Response Retrieval Approach for Dialogue Using a Multi-Attentive Transformer

December 15, 2020 · Entered Twilight · 🏛 arXiv.org

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Repo contents: .gitignore, .gitmodules, LICENSE, README.md, data, debugging.txt, mm_action_prediction, mm_response_generation, processed_data, requirements.txt, scripts, simmc, slots_type.json, tools, txt

Authors Matteo A. Senese, Alberto Benincasa, Barbara Caputo, Giuseppe Rizzo arXiv ID 2012.08148 Category cs.CL: Computation & Language Cross-listed cs.AI Citations 4 Venue arXiv.org Repository https://github.com/D2KLab/dstc9-SIMMC ⭐ 1 Last Checked 2 months ago

Abstract

This paper presents our work for the ninth edition of the Dialogue System Technology Challenge (DSTC9). Our solution addresses the track number four: Simulated Interactive MultiModal Conversations. The task consists in providing an algorithm able to simulate a shopping assistant that supports the user with his/her requests. We address the task of response retrieval, that is the task of retrieving the most appropriate agent response from a pool of response candidates. Our approach makes use of a neural architecture based on transformer with a multi-attentive structure that conditions the response of the agent on the request made by the user and on the product the user is referring to. Final experiments on the SIMMC Fashion Dataset show that our approach achieves the second best scores on all the retrieval metrics defined by the organizers. The source code is available at https://github.com/D2KLab/dstc9-SIMMC.