Maximizing SLU Performance with Minimal Training Data Using Hybrid RNN Plus Rule-based Approach

Takeshi Homma, Adriano S. Arantes, Maria Teresa Gonzalez Diaz, Masahito Togami


Abstract
Spoken language understanding (SLU) by using recurrent neural networks (RNN) achieves good performances for large training data sets, but collecting large training datasets is a challenge, especially for new voice applications. Therefore, the purpose of this study is to maximize SLU performances, especially for small training data sets. To this aim, we propose a novel CRF-based dialog act selector which chooses suitable dialog acts from outputs of RNN SLU and rule-based SLU. We evaluate the selector by using DSTC2 corpus when RNN SLU is trained by less than 1,000 training sentences. The evaluation demonstrates the selector achieves Micro F1 better than both RNN and rule-based SLUs. In addition, it shows the selector achieves better Macro F1 than RNN SLU and the same Macro F1 as rule-based SLU. Thus, we confirmed our method offers advantages in SLU performances for small training data sets.
Anthology ID:
W18-5043
Volume:
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
SIGDIAL | WS
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
366–370
Language:
URL:
https://aclanthology.org/W18-5043
DOI:
10.18653/v1/W18-5043
Bibkey:
Cite (ACL):
Takeshi Homma, Adriano S. Arantes, Maria Teresa Gonzalez Diaz, and Masahito Togami. 2018. Maximizing SLU Performance with Minimal Training Data Using Hybrid RNN Plus Rule-based Approach. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 366–370, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Maximizing SLU Performance with Minimal Training Data Using Hybrid RNN Plus Rule-based Approach (Homma et al., 2018)
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PDF:
https://aclanthology.org/W18-5043.pdf