Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives

Won Ik Cho, Youngki Moon, Sangwhan Moon, Seok Min Kim, Nam Soo Kim


Abstract
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.
Anthology ID:
2020.findings-emnlp.31
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
329–339
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.31
DOI:
10.18653/v1/2020.findings-emnlp.31
Bibkey:
Cite (ACL):
Won Ik Cho, Youngki Moon, Sangwhan Moon, Seok Min Kim, and Nam Soo Kim. 2020. Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 329–339, Online. Association for Computational Linguistics.
Cite (Informal):
Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives (Cho et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.31.pdf
Code
 warnikchow/sae4k