@inproceedings{shah-etal-2019-robust,
title = "Robust Zero-Shot Cross-Domain Slot Filling with Example Values",
author = "Shah, Darsh and
Gupta, Raghav and
Fayazi, Amir and
Hakkani-Tur, Dilek",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1547/",
doi = "10.18653/v1/P19-1547",
pages = "5484--5490",
abstract = "Task-oriented dialog systems increasingly rely on deep learning-based slot filling models, usually needing extensive labeled training data for target domains. Often, however, little to no target domain training data may be available, or the training and target domain schemas may be misaligned, as is common for web forms on similar websites. Prior zero-shot slot filling models use slot descriptions to learn concepts, but are not robust to misaligned schemas. We propose utilizing both the slot description and a small number of examples of slot values, which may be easily available, to learn semantic representations of slots which are transferable across domains and robust to misaligned schemas. Our approach outperforms state-of-the-art models on two multi-domain datasets, especially in the low-data setting."
}
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<abstract>Task-oriented dialog systems increasingly rely on deep learning-based slot filling models, usually needing extensive labeled training data for target domains. Often, however, little to no target domain training data may be available, or the training and target domain schemas may be misaligned, as is common for web forms on similar websites. Prior zero-shot slot filling models use slot descriptions to learn concepts, but are not robust to misaligned schemas. We propose utilizing both the slot description and a small number of examples of slot values, which may be easily available, to learn semantic representations of slots which are transferable across domains and robust to misaligned schemas. Our approach outperforms state-of-the-art models on two multi-domain datasets, especially in the low-data setting.</abstract>
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%0 Conference Proceedings
%T Robust Zero-Shot Cross-Domain Slot Filling with Example Values
%A Shah, Darsh
%A Gupta, Raghav
%A Fayazi, Amir
%A Hakkani-Tur, Dilek
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F shah-etal-2019-robust
%X Task-oriented dialog systems increasingly rely on deep learning-based slot filling models, usually needing extensive labeled training data for target domains. Often, however, little to no target domain training data may be available, or the training and target domain schemas may be misaligned, as is common for web forms on similar websites. Prior zero-shot slot filling models use slot descriptions to learn concepts, but are not robust to misaligned schemas. We propose utilizing both the slot description and a small number of examples of slot values, which may be easily available, to learn semantic representations of slots which are transferable across domains and robust to misaligned schemas. Our approach outperforms state-of-the-art models on two multi-domain datasets, especially in the low-data setting.
%R 10.18653/v1/P19-1547
%U https://aclanthology.org/P19-1547/
%U https://doi.org/10.18653/v1/P19-1547
%P 5484-5490
Markdown (Informal)
[Robust Zero-Shot Cross-Domain Slot Filling with Example Values](https://aclanthology.org/P19-1547/) (Shah et al., ACL 2019)
ACL