The Financial Document Causality Detection shared task (FinCausal) is a competition organized within the Financial Narrative Processing (FNP) workshop series. It aims to identify the causal relationship between a question and its answer in a given financial context. The dataset is built from real annual reports drafted by Spanish IBEX 35 companies and several UK companies. The task includes two subtasks, one in English and one in Spanish. It is formulated as an Extractive Question-Answering (EQA) task in which, given a context (C) and a question (Q), participants must extract the verbatim answer span (A). The 2026 edition introduces several changes to increase task difficulty, including the reformulation of 10% of the questions to require deeper reasoning and a stronger emphasis on multi-step causal chains with three or more elements, achieved by removing overly simple cases and adding 500 new complex fragments per language. Another innovation is the adoption of an LLM-as-a-judge metric on a 1–5 scale, based on a rubric designed to align better with human preferences than Semantic Answer Similarity (SAS) and Exact Match (EM). This edition was hosted as part of the LREC conference in Palma de Mallorca, Spain.

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.

This paper evaluates three data augmentation strategies for training misspelling detection models in Spanish. Using the Spanish CORRSIC corpus of naturally occurring misspellings, we compare three misspelling generation methods: random perturbations, keyboard-based errors, and a statistical model derived from empirical edit patterns encoded as weighted finite-state transducers. We also analyze two word selection strategies (random and length-based) and two augmentation configurations designed to balance data diversity and reduce spurious correlations. This study shows that the statistical model produces misspellings most similar to real data, showing the lowest Jensen–Shannon divergence (0.148 nats) with the empirical distribution. In downstream detection experiments, performance improves with training size, and differences between word selection strategies remain minimal. Overall, the results highlight the value of statistically grounded misspelling generation for realistic and effective data augmentation in spell-checking tasks in Spanish.