@inproceedings{pratapa-etal-2022-multilingual,
    title = "Multilingual Event Linking to {W}ikidata",
    author = "Pratapa, Adithya  and
      Gupta, Rishubh  and
      Mitamura, Teruko",
    editor = "Asai, Akari  and
      Choi, Eunsol  and
      Clark, Jonathan H.  and
      Hu, Junjie  and
      Lee, Chia-Hsuan  and
      Kasai, Jungo  and
      Longpre, Shayne  and
      Yamada, Ikuya  and
      Zhang, Rui",
    booktitle = "Proceedings of the Workshop on Multilingual Information Access (MIA)",
    month = jul,
    year = "2022",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.mia-1.5/",
    doi = "10.18653/v1/2022.mia-1.5",
    pages = "37--58",
    abstract = "We present a task of multilingual linking of events to a knowledge base. We automatically compile a large-scale dataset for this task, comprising of 1.8M mentions across 44 languages referring to over 10.9K events from Wikidata. We propose two variants of the event linking task: 1) multilingual, where event descriptions are from the same language as the mention, and 2) crosslingual, where all event descriptions are in English. On the two proposed tasks, we compare multiple event linking systems including BM25+ (Lv and Zhai, 2011) and multilingual adaptations of the biencoder and crossencoder architectures from BLINK (Wu et al., 2020). In our experiments on the two task variants, we find both biencoder and crossencoder models significantly outperform the BM25+ baseline. Our results also indicate that the crosslingual task is in general more challenging than the multilingual task. To test the out-of-domain generalization of the proposed linking systems, we additionally create a Wikinews-based evaluation set. We present qualitative analysis highlighting various aspects captured by the proposed dataset, including the need for temporal reasoning over context and tackling diverse event descriptions across languages."
}