Conversational Machine Comprehension (CMC), a research track in conversational AI, expects the machine to understand an open-domain natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. While most of the research in Machine Reading Comprehension (MRC) revolves around single-turn question answering (QA), multi-turn CMC has recently gained prominence, thanks to the advancement in natural language understanding via neural language models such as BERT and the introduction of large-scale conversational datasets such as CoQA and QuAC. The rise in interest has, however, led to a flurry of concurrent publications, each with a different yet structurally similar modeling approach and an inconsistent view of the surrounding literature. With the volume of model submissions to conversational datasets increasing every year, there exists a need to consolidate the scattered knowledge in this domain to streamline future research. This literature review attempts at providing a holistic overview of CMC with an emphasis on the common trends across recently published models, specifically in their approach to tackling conversational history. The review synthesizes a generic framework for CMC models while highlighting the differences in recent approaches and intends to serve as a compendium of CMC for future researchers.
翻译:在对话性AI(CMC)的研究轨迹中,多方向机器理解(CMC)是对话性AI(CMC)的一个研究轨迹,它希望机器能够理解开放的自然语言文本,然后进行多方向对话,回答与该文本有关的问题。虽然机器阅读综合(MRC)的大多数研究围绕单点回答(QA),但多方向计算机理解(CMC)最近越来越突出,因为通过诸如BERT等神经语言模型和采用CoQA和QuAC等大规模对话数据集,自然语言理解的进步。然而,人们的兴趣上升导致同时出版的出版物纷繁多,每种出版物都采用不同的结构类似的模式,对周围文献的看法不一致。随着对谈话性数据集的提交模型数量逐年增加,有必要巩固该领域的分散知识,以简化未来的研究。这一文献审查试图为CMC提供一个全面的概览,强调最近出版的模型的共同趋势,特别是处理谈话性历史的方法。审查综合了CMC模型的通用框架,目的是将CMC模型的近期的模型作为研究者的一种差异,同时打算将CMC模型的通用框架作为未来的研究者。