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Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots

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submitted on 2024-05-27, 07:32 and posted on 2024-05-27, 07:33 authored by Momchil Hardalov, Ivan Koychev, Preslav Nakov

Recent advances in deep neural networks, language modeling and language generation have introduced new ideas to the field of conversational agents. As a result, deep neural models such as sequence-to-sequence, memory networks, and the Transformer have become key ingredients of state-of-the-art dialog systems. While those models are able to generate meaningful responses even in unseen situations, they need a lot of training data to build a reliable model. Thus, most real-world systems have used traditional approaches based on information retrieval (IR) and even hand-crafted rules, due to their robustness and effectiveness, especially for narrow-focused conversations. Here, we present a method that adapts a deep neural architecture from the domain of machine reading comprehension to re-rank the suggested answers from different models using the question as a context. We train our model using negative sampling based on question–answer pairs from the Twitter Customer Support Dataset. The experimental results show that our re-ranking framework can improve the performance in terms of word overlap and semantics both for individual models as well as for model combinations.

Other Information

Published in: Information
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.3390/info10030082

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

MDPI

Publication Year

  • 2019

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • Qatar Computing Research Institute - HBKU

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