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    <abstract>Open-vocabulary slots, such as file name, album name, or schedule title, significantly degrade the performance of neural-based slot filling models since these slots can take on values from a virtually unlimited set and have no semantic restriction nor a length limit. In this paper, we propose a robust adversarial model-agnostic slot filling method that explicitly decouples local semantics inherent in open-vocabulary slot words from the global context. We aim to depart entangled contextual semantics and focus more on the holistic context at the level of the whole sentence. Experiments on two public datasets show that our method consistently outperforms other methods with a statistically significant margin on all the open-vocabulary slots without deteriorating the performance of normal slots.</abstract>
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%0 Conference Proceedings
%T Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots
%A Yan, Yuanmeng
%A He, Keqing
%A Xu, Hong
%A Liu, Sihong
%A Meng, Fanyu
%A Hu, Min
%A Xu, Weiran
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F yan-etal-2020-adversarial
%X Open-vocabulary slots, such as file name, album name, or schedule title, significantly degrade the performance of neural-based slot filling models since these slots can take on values from a virtually unlimited set and have no semantic restriction nor a length limit. In this paper, we propose a robust adversarial model-agnostic slot filling method that explicitly decouples local semantics inherent in open-vocabulary slot words from the global context. We aim to depart entangled contextual semantics and focus more on the holistic context at the level of the whole sentence. Experiments on two public datasets show that our method consistently outperforms other methods with a statistically significant margin on all the open-vocabulary slots without deteriorating the performance of normal slots.
%R 10.18653/v1/2020.emnlp-main.490
%U https://aclanthology.org/2020.emnlp-main.490
%U https://doi.org/10.18653/v1/2020.emnlp-main.490
%P 6070-6075

Markdown (Informal)

[Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots](https://aclanthology.org/2020.emnlp-main.490) (Yan et al., EMNLP 2020)

• Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots (Yan et al., EMNLP 2020)

ACL

• Yuanmeng Yan, Keqing He, Hong Xu, Sihong Liu, Fanyu Meng, Min Hu, and Weiran Xu. 2020. Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6070–6075, Online. Association for Computational Linguistics.