通过生成语义对抗负例，让模型更关注目标词的细微含义差异。
LANE: Lexical Adversarial Negative Examples for Word Sense Disambiguation
- 用选择性标记词汇生成难负例，引导模型聚焦目标词
- 在词义消歧和语义变化检测任务上优于对比学习基线
- 不依赖特定模型，可嵌入现有表示学习框架
细粒度词义解析仍是神经语言模型的核心挑战，因其常过度依赖全局句子表征，忽视局部语义细节。本文提出一种名为 LANE 的新型对抗训练策略，通过有选择地标记训练集中其他词语，生成具有挑战性的负样本，迫使模型在相同句子中对不同标记词建立更强区分能力。实验表明，该方法提升了词义消歧与词汇语义变化检测任务的性能，获得更具判别性的词表示。定性分析显示，该方法使模型在复杂环境下仍能捕捉细微语义差异。该方法具备模型无关性，可无缝集成至现有表示学习框架。
原文摘要 · Abstract (English)
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.
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