Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding

Samuel Louvan, Bernardo Magnini


Abstract
Slot filling is a crucial task in the Natural Language Understanding (NLU) component of a dialogue system. Most approaches for this task rely solely on the domain-specific datasets for training. We propose a joint model of slot filling and Named Entity Recognition (NER) in a multi-task learning (MTL) setup. Our experiments on three slot filling datasets show that using NER as an auxiliary task improves slot filling performance and achieve competitive performance compared with state-of-the-art. In particular, NER is effective when supervised at the lower layer of the model. For low-resource scenarios, we found that MTL is effective for one dataset.
Anthology ID:
W18-5711
Volume:
Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
74–80
Language:
URL:
https://www.aclweb.org/anthology/W18-5711
DOI:
10.18653/v1/W18-5711
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-5711.pdf