BERT-CoQAC: BERT-Based Conversational Question Answering in Context
نویسندگان
چکیده
As one promising way to inquire about any particular information through a dialog with the bot, question answering systems have gained increasing research interests recently. Designing interactive QA has always been challenging task in natural language processing and used as benchmark evaluate machine’s ability of understanding. However, such often struggle when is carried out multiple turns by users seek more based on what they already learned, thus, giving rise another complicated form called Conversational Question Answering (CQA). CQA are criticized for not understanding or utilizing previous context conversation questions. To address gap, this paper, we explore how integrate conversational history into neural machine comprehension system. On hand, introduce framework publicly available pre-trained model BERT incorporating other propose selection mechanism that selects relevant contributes most answer current question. Experimentation results revealed our comparable performance state-of-the-art models QuAC ( http://quac.ai/ ) leader board. We also conduct number experiments show side effects using entire which brings unnecessary noise signals resulting decline model’s performance.
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ژورنال
عنوان ژورنال: Communications in computer and information science
سال: 2021
ISSN: ['1865-0937', '1865-0929']
DOI: https://doi.org/10.1007/978-981-16-0010-4_5