AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modeling
It addresses the problem of making semantic change modeling more interpretable for researchers in computational linguistics and historical linguistics, though it is incremental as it builds on existing shared task frameworks.
The paper presents AXOLOTL'24, the first multilingual explainable semantic change modeling shared task, which introduced new sense-annotated datasets for Finnish and Russian and a German test set, focusing on identifying novel senses and providing definitions to enhance explainability in semantic change analysis.
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.