This paper explores bidirectional financial Machine Translation (MT) between Spanish and English, focusing on the specialized domain of annual reports from IBEX 35 companies. Fine-tuned models are compared against zero-shot scenarios through a series of experiments, testing factors such as prompting strategies and model size. On the one hand, this work studies a combination of existing fine-tuning strategies aimed at improving the adaptability of MT models to variable-sized contexts, and, on the other hand, it analyzes the limitations detected in current evaluation metrics. Results are mixed: fine-tuned models show an improvement in both short and long-context scenarios in traditional metrics, while zero-shot predictions are clearly favored by neural metrics. In fact, reference-free assessment of the source and the human reference received worse scores than the off-the-shelf prediction models. Consequently, fine-tuning on the human-made dataset hardly improves the neural metrics against zero-shot generations. This suggests that neural metrics tend to favor the fluency of MT generations and literalness over creativity, among other technical limitations regarding long-context adaptability. From a practical standpoint, the low Translation Edit Rate (TER) scores suggest that specialized fine-tuning remains the most viable path for companies to implement efficient Machine Translation Post-Editing (MTPE) workflows, given the stylistic alignment.

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.