Improving Spoken Language Understanding by Wisdom of Crowds

Spoken language understanding (SLU), which converts user requests in natural language to machine-interpretable expressions, is becoming an essential task. The lack of training data is an important problem, especially for new system tasks, because existing SLU systems are based on statistical approaches. In this paper, we proposed to use two sources of the “wisdom of crowds,” crowdsourcing and knowledge community website, for improving the SLU system. We firstly collected paraphrasing variations for new system tasks through crowdsourcing as seed data, and then augmented them using similar questions from a knowledge community website. We investigated the effects of the proposed data augmentation method in SLU task, even with small seed data. In particular, the proposed architecture augmented more than 120,000 samples to improve SLU accuracies.

• Publication year

  2020

• Venue

  International Conference on Computational Linguistics

• Publication date

  2020-12-01

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.18653/V1/2020.COLING-MAIN.234
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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