Toward zero-shot Entity Recognition in Task-oriented Conversational Agents

Marco Guerini, Simone Magnolini, Vevake Balaraman, Bernardo Magnini

Abstract
We present a domain portable zero-shot learning approach for entity recognition in task-oriented conversational agents, which does not assume any annotated sentences at training time. Rather, we derive a neural model of the entity names based only on available gazetteers, and then apply the model to recognize new entities in the context of user utterances. In order to evaluate our working hypothesis we focus on nominal entities that are largely used in e-commerce to name products. Through a set of experiments in two languages (English and Italian) and three different domains (furniture, food, clothing), we show that the neural gazetteer-based approach outperforms several competitive baselines, with minimal requirements of linguistic features.
Anthology ID:
W18-5036
Volume:
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Kazunori Komatani, Diane Litman, Kai Yu, Alex Papangelis, Lawrence Cavedon, Mikio Nakano
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
317–326
Language:
URL:
https://aclanthology.org/W18-5036
DOI:
10.18653/v1/W18-5036
Bibkey:
Cite (ACL):
Marco Guerini, Simone Magnolini, Vevake Balaraman, and Bernardo Magnini. 2018. Toward zero-shot Entity Recognition in Task-oriented Conversational Agents. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 317–326, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Toward zero-shot Entity Recognition in Task-oriented Conversational Agents (Guerini et al., SIGDIAL 2018)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/W18-5036.pdf
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