  Modern dialog managers face the challenge of having to fulfill human-level
conversational skills as part of common user expectations, including but not
limited to discourse with no clear objective. Along with these requirements,
agents are expected to extrapolate intent from the user's dialogue even when
subjected to non-canonical forms of speech. This depends on the agent's
comprehension of paraphrased forms of such utterances. Especially in
low-resource languages, the lack of data is a bottleneck that prevents
advancements of the comprehension performance for these types of agents. In
this regard, here we demonstrate the necessity of extracting the intent
argument of non-canonical directives in a natural language format, which may
yield more accurate parsing, and suggest guidelines for building a parallel
corpus for this purpose. Following the guidelines, we construct a Korean corpus
of 50K instances of question/command-intent pairs, including the labels for
classification of the utterance type. We also propose a method for mitigating
class imbalance, demonstrating the potential applications of the corpus
generation method and its multilingual extensibility.
