A Knowledge-based Approach for Answering Complex Questions in Persian
This work addresses the problem of complex question answering for Persian speakers, but it is incremental as it adapts existing methods to a new language and dataset.
The paper tackles the challenge of answering complex questions in Persian, a low-resource language, by proposing a knowledge-based approach using Farsbase and PeCoQ, and reports that it outperforms other methods in Persian complex question answering.
Research on open-domain question answering (QA) has a long tradition. A challenge in this domain is answering complex questions (CQA) that require complex inference methods and large amounts of knowledge. In low resource languages, such as Persian, there are not many datasets for open-domain complex questions and also the language processing toolkits are not very accurate. In this paper, we propose a knowledge-based approach for answering Persian complex questions using Farsbase; the Persian knowledge graph, exploiting PeCoQ; the newly created complex Persian question dataset. In this work, we handle multi-constraint and multi-hop questions by building their set of possible corresponding logical forms. Then Multilingual-BERT is used to select the logical form that best describes the input complex question syntactically and semantically. The answer to the question is built from the answer to the logical form, extracted from the knowledge graph. Experiments show that our approach outperforms other approaches in Persian CQA.