Improving Spoken Language Understanding by Wisdom of Crowds
Koichiro Yoshino, Kana Ikeuchi, Katsuhito Sudoh, Satoshi Nakamura
Abstract
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.
BibTeX
@inproceedings{yoshino-etal-2020-improving,
title = "Improving Spoken Language Understanding by Wisdom of Crowds",
author = "Yoshino, Koichiro and
Ikeuchi, Kana and
Sudoh, Katsuhito and
Nakamura, Satoshi",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.234/",
doi = "10.18653/v1/2020.coling-main.234",
pages = "2606--2612"
}