AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modeling

TL;DR

AXOLOTL'24 is a multilingual shared task focused on explainable semantic change modeling, introducing new datasets and subtasks for identifying and defining evolving word senses across languages.

Contribution

It presents new sense-annotated datasets for Finnish and Russian, a German test dataset, and a novel setup involving sense discovery and explanation tasks.

Findings

Winning methods demonstrate effective sense identification and explanation.

The setup advances explainability in semantic change modeling.

New datasets facilitate multilingual and explainable research.

Abstract

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.