@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://preview.aclanthology.org/add-emnlp-2024-awards/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."
}