@inproceedings{louvan-magnini-2018-exploring,
title = "Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding",
author = "Louvan, Samuel and
Magnini, Bernardo",
editor = "Chuklin, Aleksandr and
Dalton, Jeff and
Kiseleva, Julia and
Borisov, Alexey and
Burtsev, Mikhail",
booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5711",
doi = "10.18653/v1/W18-5711",
pages = "74--80",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding
%A Louvan, Samuel
%A Magnini, Bernardo
%Y Chuklin, Aleksandr
%Y Dalton, Jeff
%Y Kiseleva, Julia
%Y Borisov, Alexey
%Y Burtsev, Mikhail
%S Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F louvan-magnini-2018-exploring
%X 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.
%R 10.18653/v1/W18-5711
%U https://aclanthology.org/W18-5711
%U https://doi.org/10.18653/v1/W18-5711
%P 74-80
Markdown (Informal)
[Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding](https://aclanthology.org/W18-5711) (Louvan & Magnini, EMNLP 2018)
ACL