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    <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 – HYbrid Distantly Supervised Relation Extraction 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.</abstract>
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%0 Conference Proceedings
%T Combining Distantly Supervised Models with In Context Learning for Monolingual and Cross-Lingual Relation Extraction
%A Rathore, Vipul Kumar
%A Faisal, Malik Hammad
%A Singla, Parag
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%A Mausam
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F rathore-etal-2026-combining
%X 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 – HYbrid Distantly Supervised Relation Extraction 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.
%U https://aclanthology.org/2026.acl-long.2109/
%P 45483-45509

Markdown (Informal)

[Combining Distantly Supervised Models with In Context Learning for Monolingual and Cross-Lingual Relation Extraction](https://aclanthology.org/2026.acl-long.2109/) (Rathore et al., ACL 2026)

• Combining Distantly Supervised Models with In Context Learning for Monolingual and Cross-Lingual Relation Extraction (Rathore et al., ACL 2026)

ACL

• Vipul Kumar Rathore, Malik Hammad Faisal, Parag Singla, and Mausam. 2026. Combining Distantly Supervised Models with In Context Learning for Monolingual and Cross-Lingual Relation Extraction. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 45483–45509, San Diego, California, United States. Association for Computational Linguistics.