Iterative Delexicalization for Improved Spoken Language Understanding

By Avik Ray, Yilin Shen, Hongxia Jin. 2019

8 citations recorded by the source corpus.

Recurrent neural network (RNN) based joint intent classification and slot tagging models have achieved tremendous success in recent years for building spoken language understanding and dialog systems. However, these models suffer from poor performance for slots which often encounter large semantic variability in slot values after deployment (e.g. message texts, partial movie/artist names). While greedy delexicalization of slots in the input utterance via substring matching can partly improve performance, it often produces incorrect input. Moreover, such techniques cannot delexicalize slots with out-of-vocabulary slot values not seen at training. In this paper, we propose a novel iterative delexicalization algorithm, which can accurately delexicalize the input, even with out-of-vocabulary slot values. Based on model confidence of the current delexicalized input, our algorithm improves delexicalization in every iteration to converge to the best input having the highest confidence. We show on benchmark and in-house datasets that our algorithm can greatly improve parsing performance for RNN based models, especially for out-of-distribution slot values.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Xiangyu Zeng
2. Hongxia Jin
3. Yilin Shen
4. Yixuan Li
5. Zixuan Ke

Citations in the corpus

• A Progressive Model to Enable Continual Learning for Semantic Slot Filling (2019, background): “…model independently (Yao et al., 2014; Peng et al., 2015; Kurata et al., 2016; Hakkani-T¨ur et al., 2016; Liu and Lane, 2016; Deng et al., 2019; Ray et al., 2019) or jointly with the other intent detection task in SLU (Guo et al., 2014; Liu and Lane, 2016; Zhang and Wang, 2016; Wang et al.,…”