2025/11/14 by Jader Martins Camboim de Sá, Jooyoung Lee, de Sá, Jader Martins Camboim +5
Computer Science · Social Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Language and cultural evolution #Natural Language Processing Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2511.11234
openalex publication_date 2025/11/14 · openalex created_date 2025/11/18 · openalex updated_date 2026/07/28
Fine-grained word meaning resolution remains a critical challenge for neural language models (NLMs) as they often overfit to global sentence representations, failing to capture local semantic details. We propose a novel adversarial training strategy, called LANE, to address this limitation by deliberately shifting the model's learning focus to the target word. This method generates challenging negative training examples through the selective marking of alternate words in the training set. The goal is to force the model to create a greater separability between same sentences with different marked words. Experimental results on lexical semantic change detection and word sense disambiguation benchmarks demonstrate that our approach yields more discriminative word representations, improving performance over standard contrastive learning baselines. We further provide qualitative analyses showing that the proposed negatives lead to representations that better capture subtle meaning differences even in challenging environments. Our method is model-agnostic and can be integrated into existing representation learning frameworks.