We present the Financial Document Causality Detection Task (FinCausal 2025), a multilingual challenge to identify causal relationships within financial texts. This task comprises English and Spanish subtasks, with datasets compiled from British and Spanish annual reports. Participants were tasked with identifying and generating answers to questions about causes or effects within specific text segments. The dataset combines extractive and generative question-answering (QA) methods, with abstractly formulated questions and directly extracted answers from the text. Systems performance is evaluated using exact matching and semantic similarity metrics. The challenge attracted submissions from 10 teams for the English subtask and 10 teams for the Spanish subtask. FinCausal 2025 is part of the 6th Financial Narrative Processing Workshop (FNP 2025), hosted at COLING 2025 in Abu Dhabi.

Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment is carried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships such as broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide range of languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data will pave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriously requiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.