Dialo-AP: A Dependency Parsing Based Argument Parser for Dialogues
Dialo-AP: A Dependency Parsing Based Argument Parser for Dialogues
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发表时间：
2022
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通讯作者：
Sougata Saha;Souvik Das;R. Srihari
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作者：
Sougata Saha;Souvik Das;R. Srihari
虽然神经方法的参数挖掘（AM）已经取得了很大的进步，但最近的工作大多数仅限于解析独白。随着人们对使用会话代理进行更广泛的社会应用的迫切兴趣，有必要推进对话的参数解析器的最新发展。这使我们能够进行更有目的的对话，包括说服、辩论和审议。本文讨论了Dialo-AP，一个端到端的参数解析器，从对话中构造参数图。我们将AM表述为基本和论证性话语单元的依存解析;该系统是使用包括九个不同语料库的广泛预训练和课程学习进行训练的。Dialo-AP能够通过执行AM的所有子任务从对话中生成参数图。与现有的最先进的基线相比，Dialo-AP在所有任务中都实现了显着改进，并通过严格的人工评估进一步验证。
While neural approaches to argument mining (AM) have advanced considerably, most of the recent work has been limited to parsing monologues. With an urgent interest in the use of conversational agents for broader societal applications, there is a need to advance the state-of-the-art in argument parsers for dialogues. This enables progress towards more purposeful conversations involving persuasion, debate and deliberation. This paper discusses Dialo-AP, an end-to-end argument parser that constructs argument graphs from dialogues. We formulate AM as dependency parsing of elementary and argumentative discourse units; the system is trained using extensive pre-training and curriculum learning comprising nine diverse corpora. Dialo-AP is capable of generating argument graphs from dialogues by performing all sub-tasks of AM. Compared to existing state-of-the-art baselines, Dialo-AP achieves significant improvements across all tasks, which is further validated through rigorous human evaluation.