Abstract
Intention identification is a core issue in dialog management. However, due to the non-canonicality of the spoken language, it is difficult to extract the content automatically from the conversation-style utterances. This is much harder for languages like Korean and Japanese since the agglutination between morphemes make it difficult for the machines to parse the sentence and understand the intention. To suggest a guideline for this problem, inspired by the neural summarization systems introduced recently, we propose a structured annotation scheme for Korean questions/commands which is widely applicable to the field of argument extraction. The annotated corpus is freely available online, and the corpus is additionally tagged with syntactical information for further usage.
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URL
http://arxiv.org/abs/1810.04631