A BERT-based Question Answering Architecture for Spanish Language

Research output: Contribution to journalArticlepeer-review

Abstract

QA systems have had various approaches to achieve their goal of solving naturally formed questions, recent works use state of the art techniques such as neural networks, QA systems in different languages are increasing, as evidenced, they are advancing at different rates, despite the fact that there are efforts to increase research in this type of systems. In this research we analyze the main aspects of the contributions to Question Answering and present an architecture that is capable of answering questions in Spanish. The initial question is received by the system, which may or may not have a document corpus from which to extract the answer, if it does not have a specified document corpus, the Answer Generation module returns the answer to the initial question. The purpose of the system is to provide answers to factoid questions posed by users through a web and mobile platform. BI-LSTM was used for document retrieval and BERT was used to generate the answers. We tested the architecture with ten thousand questions reaching an accuracy of 0.7856.

Original languageEnglish
Pages (from-to)119-127
Number of pages9
JournalInternational Journal of Computer Information Systems and Industrial Management Applications
Volume14
StatePublished - 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© MIR Labs, www.mirlabs.net/ijcisim/index.html

Keywords

  • Artificial intelligence
  • Information retrieval
  • Learning systems
  • Machine learning
  • Neural networks

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