Publication
Overview of #SMM4H 2024 -- Task 2: Cross-Lingual Few-Shot Relation Extraction for Pharmacovigilance in French, German, and Japanese
Lisa Raithel; Philippe Thomas; Bhuvanesh Verma; Roland Roller; Hui-Syuan Yeh; Shuntaro Yada; Cyril Grouin; Shoko Wakamiya; Eiji Aramaki; Sebastian Möller; Pierre Zweigenbaum
In: Dongfang Xu; Graciela Gonzalez-Hernandez (Hrsg.). Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks. Social Media Mining for Health Research and Applications Workshop (SMM4H-2024), located at ACL 2024, August 15, Bangkok, Thailand, Pages 170-182, Association for Computational Linguistics, 2024.
Abstract
This paper provides an overview of Task 2 from the Social Media Mining for Health 2024 shared task (#SMM4H 2024), which focused on Named Entity Recognition (NER, Subtask 2a) and the joint task of NER and Relation Extraction (RE, Subtask 2b) for detecting adverse drug reactions (ADRs) in German, Japanese, and French texts written by patients. Participants were challenged with a few-shot learning scenario, necessitating models that can effectively generalize from limited annotated examples. Despite the diverse strategies employed by the participants, the overall performance across submissions from three teams highlighted significant challenges. The results underscored the complexity of extracting entities and relations in multi-lingual contexts, especially from the noisy and informal nature of user-generated content. Further research is required to develop robust systems capable of accurately identifying and associating ADR-related information in low-resource and multilingual settings.