@inproceedings{rathore-etal-2026-combining,
    title = "Combining Distantly Supervised Models with In Context Learning for Monolingual and Cross-Lingual Relation Extraction",
    author = "Rathore, Vipul Kumar  and
      Faisal, Malik Hammad  and
      Singla, Parag  and
      Mausam",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.2109/",
    pages = "45483--45509",
    ISBN = "979-8-89176-390-6",
    abstract = "Distantly Supervised Relation Extraction (DSRE) remains a long-standing challenge in NLP, where models must learn from noisy bag-level annotations while making sentence-level predictions. While existing state-of-the-art (SoTA) DSRE models rely on task-specific training, their integration with in-context learning (ICL) using large language models (LLMs) remains underexplored. A key challenge is that the LLM may not learn relation semantics correctly, due to noisy annotation.In response, we propose $HYDRE$ {--} $HY$brid $D$istantly Supervised $R$elation $E$xtraction framework. It first uses a trained DSRE model to identify the top-$k$ candidate relations for a given test sentence, then uses a novel dynamic exemplar retrieval strategy that extracts reliable, sentence-level exemplars from training data, which are then provided in LLM prompt for outputting the final relation(s).We further extend $HYDRE$ to cross-lingual settings for RE in low-resource languages. Using available English DSRE training data, we evaluate all methods on English as well as a newly curated benchmark covering four diverse low-resource Indic languages - Oriya, Santali, Manipuri, and Tulu. $HYDRE$ achieves up to 20 F1 point gains in English and, on average, 17 F1 points on Indic languages over prior SoTA DSRE models and naive prompting baselines. Detailed ablations exhibit $HYDRE${'}s efficacy compared to other prompting strategies."
}