Large-scale Translation to Enable Response Selection in Low Resource Languages: A COVID-19 Chatbot Experiment

  • Lucas Almeida Aguiar Universidade Estadual do Ceará (UECE)
  • Lívia Almada Cruz Universidade Federal do Ceará (UFC)
  • Ticiana L. Coelho da Silva Universidade Federal do Ceará (UFC)
  • Rafael Augusto Ferreira do Carmo Universidade Federal do Ceará (UFC)
  • Matheus Henrique Esteves Paixao Universidade Estadual do Ceará (UECE)


Natural Language Processing for Low Resource Languages is challenging. The lack of large-scale datasets affects the performance of data-hungry algorithms. To overcome this, we employ data augmentation to enlarge the training data for the task of response selection in multi-turn retrieval-based chatbots. We automatically translated a large-scale English dataset to Brazilian Portuguese (PT_BR) and used it to train a deep neural network. For a COVID-19 chatbot system, our results show that the combination of training with the translated dataset followed by a fine-tuning with the context-specific dataset provides the best results in terms of recall for all studied models. In addition, we make available the translated large-scale PT_BR dataset.

Palavras-chave: natural language processing, automatic translation, multi-turn retrieval-based chatbot, low resource language


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AGUIAR, Lucas Almeida; CRUZ, Lívia Almada; SILVA, Ticiana L. Coelho da; CARMO, Rafael Augusto Ferreira do; PAIXAO, Matheus Henrique Esteves. Large-scale Translation to Enable Response Selection in Low Resource Languages: A COVID-19 Chatbot Experiment. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 37. , 2022, Búzios. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 203-215. ISSN 2763-8979. DOI: