Arabic Medical Community Question Answering Using ON-LSTM and CNN

Husamelddin A.M.N. Balla, Marisa Llorens Salvador, Sarah Jane Delany

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we address the problem of Arabic community question answering. We propose a model that leverages both the archived question and answer representations in the similarity computation with the user's question. The proposed model considers the interaction of the user's question with both archived questions and answers separately to address the noisy information problem in Arabic community question answering. The proposed model is a combination of two parts that covers question-question similarity and question-answer relevance. We used twin ON-LSTM with an attention mechanism and Arabic ELMo embeddings as input for the question-question similarity. For the question-answer relevance, we used a combination of twin ON-LSTM and CNN networks which can capture the relevance score even with long answers and questions. We evaluated the proposed model on the biomedical Arabic community question answering dataset cQA-MD. The proposed model outperformed the previous studies evaluated on the same dataset.

Original languageEnglish
Title of host publication2022 14th International Conference on Machine Learning and Computing, ICMLC 2022
PublisherAssociation for Computing Machinery
Pages298-307
Number of pages10
ISBN (Electronic)9781450395700
DOIs
Publication statusPublished - 18 Feb 2022
Event14th International Conference on Machine Learning and Computing, ICMLC 2022 - Virtual, Online, China
Duration: 18 Feb 202221 Feb 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference14th International Conference on Machine Learning and Computing, ICMLC 2022
Country/TerritoryChina
CityVirtual, Online
Period18/02/2221/02/22

Keywords

  • Answer ranking
  • Arabic QA
  • Community question answering

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