LANE：用于词义消歧的词汇对抗负样本
LANE: Lexical Adversarial Negative Examples for Word Sense Disambiguation
AI总结：
针对神经语言模型易过拟合全局句子表征、难以捕捉局部语义细节的问题，提出对抗训练策略LANE，通过生成选择性标记替换词的负样本提升词义区分能力，在相关基准上性能优于对比学习基线，且模型无关可集成。
摘要：
细粒度词义解析仍是神经语言模型（NLM）面临的一项关键挑战，因为这类模型往往会对全局句子表征过拟合，无法捕捉局部语义细节。我们提出了一种名为LANE的新型对抗训练策略，通过有意将模型的学习焦点转移到目标词上来解决这一局限。该方法通过选择性标记训练集中的替换词来生成具有挑战性的负训练样本，目标是迫使模型提升标记不同词的同一句子之间的可分离度。在词汇语义变化检测和词义消歧基准上的实验结果表明，我们的方法能生成区分度更高的词表征，性能优于标准对比学习基线。我们进一步开展的定性分析显示，所提出的负样本所生成的表征即便在具有挑战性的环境中也能更好地捕捉细微的词义差异。我们的方法与模型无关，可集成到现有的表征学习框架中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
