@inproceedings{dutt-etal-2025-dependency,
    title = "Can dependency parses facilitate generalization in language models? A case study of cross-lingual relation extraction",
    author = "Dutt, Ritam  and
      Sural, Shounak  and
      Rose, Carolyn",
    editor = "Shi, Weijia  and
      Yu, Wenhao  and
      Asai, Akari  and
      Jiang, Meng  and
      Durrett, Greg  and
      Hajishirzi, Hannaneh  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 4th International Workshop on Knowledge-Augmented Methods for Natural Language Processing",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.knowledgenlp-1.27/",
    pages = "317--337",
    ISBN = "979-8-89176-229-9",
    abstract = "In this work, we propose DEPGEN, a framework for evaluating the generalization capabilities of language models on the task of relation extraction, with dependency parses as scaffolds. We use a GNN-based framework that takes dependency parses as input and learns embeddings of entities which are augmented to a baseline multilingual encoder. We also investigate the role of dependency parses when they are included as part of the prompt to LLMs in a zero-shot learning setup. We observe that including off-the-shelf dependency parses can aid relation extraction, with the best performing model having a mild relative improvement of 0.91{\%} and 1.5{\%} in the in-domain and zero-shot setting respectively across two datasets. For the in-context learning setup, we observe an average improvement of 1.67{\%}, with significant gains for low-performing LLMs. We also carry out extensive statistical analysis to investigate how different factors such as the choice of the dependency parser or the nature of the prompt impact performance. We make our code and results publicly available for the research community at https://github.com/ShoRit/multilingual-re.git."
}