Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH scholarly article en Gonçalo Oliveira, Hugo; Inácio, Sara; Silva, Catarina https://www.dagstuhl.de/oasics License: Creative Commons Attribution 4.0 license (CC BY 4.0)
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URN: urn:nbn:de:0030-drops-167652
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Analysing Off-The-Shelf Options for Question Answering with Portuguese FAQs

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Abstract

Following the current interest in developing automatic question answering systems, we analyse alternative approaches for finding suitable answers from a list of Frequently Asked Questions (FAQs), in Portuguese. These rely on different technologies, some more established and others more recent, and are all easily adaptable to new lists of FAQs, on new domains. We analyse the effort required for their configuration, the accuracy of their answers, and the time they take to get such answers. We conclude that traditional Information Retrieval (IR) can be a solution for smaller lists of FAQs, but approaches based on deep neural networks for sentence encoding are at least as reliable and less dependent on the number and complexity of the FAQs. We also contribute with a small dataset of Portuguese FAQs on the domain of telecommunications, which was used in our experiments.

BibTeX - Entry

@InProceedings{goncalooliveira_et_al:OASIcs.SLATE.2022.19,
  author =	{Gon\c{c}alo Oliveira, Hugo and In\'{a}cio, Sara and Silva, Catarina},
  title =	{{Analysing Off-The-Shelf Options for Question Answering with Portuguese FAQs}},
  booktitle =	{11th Symposium on Languages, Applications and Technologies (SLATE 2022)},
  pages =	{19:1--19:11},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-245-7},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{104},
  editor =	{Cordeiro, Jo\~{a}o and Pereira, Maria Jo\~{a}o and Rodrigues, Nuno F. and Pais, Sebasti\~{a}o},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/16765},
  URN =		{urn:nbn:de:0030-drops-167652},
  doi =		{10.4230/OASIcs.SLATE.2022.19},
  annote =	{Keywords: Natural Language Processing, Portuguese, Question Answering, FAQs, Information Retrieval, Sentence Encoding, Transformers}
}

Keywords: Natural Language Processing, Portuguese, Question Answering, FAQs, Information Retrieval, Sentence Encoding, Transformers
Seminar: 11th Symposium on Languages, Applications and Technologies (SLATE 2022)
Issue date: 2022
Date of publication: 27.07.2022
Supplementary Material: Software (Source Code): https://github.com/NLP-CISUC/PT_QA_Agents


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