@inproceedings{taniguchi-etal-2018-joint,
    title = "Joint Modeling for Query Expansion and Information Extraction with Reinforcement Learning",
    author = "Taniguchi, Motoki  and
      Miura, Yasuhide  and
      Ohkuma, Tomoko",
    editor = "Thorne, James  and
      Vlachos, Andreas  and
      Cocarascu, Oana  and
      Christodoulopoulos, Christos  and
      Mittal, Arpit",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/W18-5506/",
    doi = "10.18653/v1/W18-5506",
    pages = "34--39",
    abstract = "Information extraction about an event can be improved by incorporating external evidence. In this study, we propose a joint model for pseudo-relevance feedback based query expansion and information extraction with reinforcement learning. Our model generates an event-specific query to effectively retrieve documents relevant to the event. We demonstrate that our model is comparable or has better performance than the previous model in two publicly available datasets. Furthermore, we analyzed the influences of the retrieval effectiveness in our model on the extraction performance."
}