Robust Zero-Shot Cross-Domain Slot Filling with Example Values

Autor:	Shah, Darsh J, Gupta, Raghav, Fayazi, Amir A, Hakkani-Tur, Dilek
Rok vydání:	2019
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: To appear in ACL 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1906.06870 Zobrazit plný text záznamu View this record from Arxiv