AXOLOTL'24 多语言可解释语义变化建模共享任务
AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modeling
AI总结：
本文介绍首个多语言可解释语义变化建模共享任务 AXOLOTL'24，发布芬兰语和俄语新数据集，并通过识别新词义和生成词典式定义推动语义变化计算方法的可解释性。
摘要：
本文描述了 AXOLOTL'24 的组织与发现，这是首个多语言可解释语义变化建模共享任务。我们发布了用于该共享任务的芬兰语和俄语新型词义标注历时语义变化数据集，以及一个借用自现有来源的仅测试德语惊喜数据集。AXOLOTL'24 的设置对语义变化建模领域而言是全新的，涉及识别未知（新）词义并为这些词义提供类词典定义的子任务。本文描述并比较了获胜团队的方法，从而为历史意义变化的计算方法实现可解释性铺平了道路。
英文摘要：
This paper describes the organization and findings of AXOLOTL'24, the first multilingual explainable semantic change modeling shared task. We present new sense-annotated diachronic semantic change datasets for Finnish and Russian which were employed in the shared task, along with a surprise test-only German dataset borrowed from an existing source. The setup of AXOLOTL'24 is new to the semantic change modeling field, and involves subtasks of identifying unknown (novel) senses and providing dictionary-like definitions to these senses. The methods of the winning teams are described and compared, thus paving a path towards explainability in computational approaches to historical change of meaning.
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。
