A Deep Learning Model of Multiple Knowledge Sources Integration for Community Question Answering
Authors: Tu Nguyen Van , Cuong Le Anh 
Published: December 2022

The community Question Answering (cQA) problem requires the task that given a question it aims at selecting the most related question-answer tuples (a question and its answers) from the stored question-answer tuples data set. The core mission of this task is to measure the similarity (or relationship) between an input question and questions from the given question-answer data set. Under our observation, there are either various information sources as well as di erent measurement models which can provide complementary knowledge for indicating the relationship between questions and question-answer tuples. In this paper we address the problem of modeling and combining multiple knowledge sources for determining and ranking the most related question-answer tuples given an input question for cQA problem. Our proposed model will generate di erent features based on di erent representations of the data as well as on di erent methods and then integrate this information into the BERT model for similarity measurement in cQA problem. We evaluate our proposed model on the SemEval 2016 data set and achieve the state-of-the-art result.

Keywords

Community question answering, Multi knowledge sources, Deep learning, The BERT model

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