CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Source-Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language
Bai, He1,2; Zhou, Yu1,2; Zhang, Jiajun1,2; Zhao, Liang3; Hwang, Mei-Yuh3; Zong, Chengqing1,2,4
Conference NameProceedings of the 27th International Conference on Computational Linguistics (COLING)
Conference DateAugust 20th-26th, 2018
Conference PlaceSanta Fe, New-Mexico, USA

To deploy a spoken language understanding (SLU) model to a new language, language transfer- ring is desired to avoid the trouble of acquiring and labeling a new big SLU corpus. Translating the original SLU corpus into the target language is an attractive strategy. However, SLU cor- pora consist of plenty of semantic labels (slots), which general-purpose translators cannot handle well, not to mention additional culture differences. This paper focuses on the language trans- ferring task given a tiny in-domain parallel SLU corpus. The in-domain parallel corpus can be used as the first adaptation on the general translator. But more importantly, we show how to use reinforcement learning (RL) to further finetune the adapted translator, where translated sen- tences with more proper slot tags receive higher rewards. We evaluate our approach on Chinese to English language transferring for SLU systems. The experimental results show that the gen- erated English SLU corpus via adaptation and reinforcement learning gives us over 97% in the slot F1 score and over 84% accuracy in domain classification. It demonstrates the effectiveness of the proposed language transferring method. Compared with naive translation, our proposed method improves domain classification accuracy by relatively 22%, and the slot filling F1 score by relatively more than 71%.

Indexed ByEI
Document Type会议论文
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation
2.University of Chinese Academy of Sciences
3.Mobvoi AI Lab
4.CAS Center for Excellence in Brain Science and Intelligence Technology
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Bai, He,Zhou, Yu,Zhang, Jiajun,et al. Source-Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language[C],2018.
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