Analysing Off-The-Shelf Options for Question Answering with Portuguese FAQs

Autor:	Gonçalo Oliveira, Hugo, Inácio, Sara, Silva, Catarina
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	Portuguese FAQs Transformers Information Retrieval Sentence Encoding Computing methodologies → Natural language processing Question Answering Natural Language Processing
DOI:	10.4230/oasics.slate.2022.19
Popis:	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. OASIcs, Vol. 104, 11th Symposium on Languages, Applications and Technologies (SLATE 2022), pages 19:1-19:11
Databáze:	OpenAIRE
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