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; Zhou, Yu1; Zhang, Jiajun1; Zhao, Liang2; Hwang, Mei-Yuh2; Zong, Chengqing1
2018
会议名称COLING
会议日期2018-8
会议地点USA
摘要

To deploy a spoken language understanding (SLU) model to a new language, language transferring 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 corpora 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 transferring 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 sentences 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 generated 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%.

语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/23207
专题模式识别国家重点实验室_自然语言处理
作者单位1.中国科学院自动化研究所
2.出门问问
第一作者单位中国科学院自动化研究所
推荐引用方式
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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