BERT-CoQAC: BERT-based Conversational Question Answering in Context

• URL: http://arxiv.org/abs/2104.11394v1
• Date: Fri, 23 Apr 2021 03:05:17 GMT
• Title: BERT-CoQAC: BERT-based Conversational Question Answering in Context
• Authors: Munazza Zaib and Dai Hoang Tran and Subhash Sagar and Adnan Mahmood and Wei E. Zhang and Quan Z. Sheng
• Abstract summary: We introduce a framework based on a publically available pre-trained language model called BERT for incorporating history turns into the system. Experiment results revealed that our framework is comparable in performance with the state-of-the-art models on the QuAC leader board.
• Score: 10.811729691130349
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As one promising way to inquire about any particular information through a dialog with the bot, question answering dialog systems have gained increasing research interests recently. Designing interactive QA systems has always been a challenging task in natural language processing and used as a benchmark to evaluate a machine's ability of natural language understanding. However, such systems often struggle when the question answering is carried out in multiple turns by the users to seek more information based on what they have already learned, thus, giving rise to another complicated form called Conversational Question Answering (CQA). CQA systems are often criticized for not understanding or utilizing the previous context of the conversation when answering the questions. To address the research gap, in this paper, we explore how to integrate conversational history into the neural machine comprehension system. On one hand, we introduce a framework based on a publically available pre-trained language model called BERT for incorporating history turns into the system. On the other hand, we propose a history selection mechanism that selects the turns that are relevant and contributes the most to answer the current question. Experimentation results revealed that our framework is comparable in performance with the state-of-the-art models on the QuAC leader board. We also conduct a number of experiments to show the side effects of using entire context information which brings unnecessary information and noise signals resulting in a decline in the model's performance.

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