@inproceedings{omori-komachi-2019-multi,
    title = {Multi-Task Learning for Japanese Predicate Argument Structure Analysis},
    author = {Omori, Hikaru  and
      Komachi, Mamoru},
    booktitle = {Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
    month = jun,
    year = {2019},
    address = {Minneapolis, Minnesota},
    publisher = {Association for Computational Linguistics},
    url = {https://www.aclweb.org/anthology/N19-1344},
    doi = {10.18653/v1/N19-1344},
    pages = {3404--3414},
    abstract = {An event-noun is a noun that has an argument structure similar to a predicate. Recent works, including those considered state-of-the-art, ignore event-nouns or build a single model for solving both Japanese predicate argument structure analysis (PASA) and event-noun argument structure analysis (ENASA). However, because there are interactions between predicates and event-nouns, it is not sufficient to target only predicates. To address this problem, we present a multi-task learning method for PASA and ENASA. Our multi-task models improved the performance of both tasks compared to a single-task model by sharing knowledge from each task. Moreover, in PASA, our models achieved state-of-the-art results in overall F1 scores on the NAIST Text Corpus. In addition, this is the first work to employ neural networks in ENASA.},
}