LeedsMEng26: Qwen + Gemini for FinCausal 2026 Causality Detection in Financial Narrative Texts
Year: 2026
Type: conference-paper
Abstract: This paper presents the LeedsMEng26 system for the FinCausal 2026 shared task Moreno-Sandoval et al. (2026a) on financial causality detection in narrative texts.The task is formulated as extractive question answering over English and Spanish financial reports, where systems must return a verbatim span from the context that answers an abstractive question about a cause or an effect.We propose a two-stage pipeline consisting of candidate span generation followed by span verification and boundary refinement under a strict extractiveness constraint.We evaluate both an extractive RoBERTa-based baseline and instruction-tuned large language models.Results show that Qwen-2.5-1.5B-Instruct is a stronger candidate generator than the RoBERTa baseline, and that a second-stage verifier further improves answer boundary accuracy and overall adequacy.Our best configuration, Qwen-2.5-1.5B-Instruct with Gemini-2.5-flashrefinement, achieved an adequacy score of 4.7000 for English and 4.6143 for Spanish.These findings suggest that a modular generation-and-verification pipeline is effective for extractive financial causality detection.
Institution: University of Leeds
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166568231