Abstract
Conversational Question Answering (ConvQA) requires a deep understanding of conversation history to answer the current question. However, most existing works ignore the sequential dependencies among history turns and treat all history indiscriminately. We propose a Flow based Context-Aware Question Answering model to alleviate the above problems. In specific, we first use a hierarchical history selector to filter out irrelated history turns according to the features of word level, utterance level and dialogue level. Then we introduce a FlowRNN to model the sequential dependencies between history turns along dialog direction. Finally we incorporate these hidden dependencies to BERT for answer predictions. Experiments on a large-scale conversational question answering dataset QuAC show that our proposed method can use conversation history effectively and outperforms most of the recent ConvQA models.
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Song, D., Yang, Y., Fang, A. (2021). A Context-Aware Model with Flow Mechanism for Conversational Question Answering. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_72
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DOI: https://doi.org/10.1007/978-3-030-92310-5_72
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