@inproceedings{jay-etal-2026-versa,
title = "{VERSA}: Verbatim Extraction via Rephrasing and Self-Aggregation for Financial Causality",
author = "Jay, Aldan and
Berlanga, Rafael and
Moreno, Yoelvis and
Santamarta, Vicent",
editor = "El-Haj, Mo and
Moreno Sandoval, Antonio and
Garcia-Serrano, Ana and
Chen, Chung-Chi and
Rayson, Paul and
Torterolo Orta, Yanco Amor and
Martinez, Paloma and
Porta, Jordi",
booktitle = "The 7th Financial Narrative Processing Workshop",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.fnp-1.14/",
doi = "10.63317/523w58xpfhbz",
pages = "139--145",
abstract = "Financial causality detection,the task of identifying and extracting verbatim causal spans from financial narratives, remains a challenging problem in Natural Language Processing (NLP). Large Language Models (LLMs), while powerful reasoners, frequently paraphrase source text or produce imprecise span boundaries when used in zero-shot extraction settings, leading to poor Exact Match scores. In this paper, we present VERSA, our system for the FinCausal 2026 Shared Task, a multi-agent pipeline that integrates two complementary inference strategies: Rephrase-and-Respond (RaR) and Recursive Self-Aggregation (RSA). The pipeline decomposes the extraction task into five sequential stages, each handled by a specialised agent: (1) causal structure analysis, (2) question reformulation via RaR, (3) diverse candidate population generation, (4) iterative refinement through RSA, and (5) verbatim validation with word-boundary alignment. We evaluate our approach on both the English and Spanish subsets of the FinCausal 2026 dataset. An ablation study demonstrates the individual and combined ontributions of RaR and RSA, showing that the full pipeline substantially outperforms a zero-shot baseline in Exact Match and token-level F1."
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<abstract>Financial causality detection,the task of identifying and extracting verbatim causal spans from financial narratives, remains a challenging problem in Natural Language Processing (NLP). Large Language Models (LLMs), while powerful reasoners, frequently paraphrase source text or produce imprecise span boundaries when used in zero-shot extraction settings, leading to poor Exact Match scores. In this paper, we present VERSA, our system for the FinCausal 2026 Shared Task, a multi-agent pipeline that integrates two complementary inference strategies: Rephrase-and-Respond (RaR) and Recursive Self-Aggregation (RSA). The pipeline decomposes the extraction task into five sequential stages, each handled by a specialised agent: (1) causal structure analysis, (2) question reformulation via RaR, (3) diverse candidate population generation, (4) iterative refinement through RSA, and (5) verbatim validation with word-boundary alignment. We evaluate our approach on both the English and Spanish subsets of the FinCausal 2026 dataset. An ablation study demonstrates the individual and combined ontributions of RaR and RSA, showing that the full pipeline substantially outperforms a zero-shot baseline in Exact Match and token-level F1.</abstract>
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%0 Conference Proceedings
%T VERSA: Verbatim Extraction via Rephrasing and Self-Aggregation for Financial Causality
%A Jay, Aldan
%A Berlanga, Rafael
%A Moreno, Yoelvis
%A Santamarta, Vicent
%Y El-Haj, Mo
%Y Moreno Sandoval, Antonio
%Y Garcia-Serrano, Ana
%Y Chen, Chung-Chi
%Y Rayson, Paul
%Y Torterolo Orta, Yanco Amor
%Y Martinez, Paloma
%Y Porta, Jordi
%S The 7th Financial Narrative Processing Workshop
%D 2026
%8 May
%I European Language Resources Association (ELRA)
%C Palma de Mallorca, Spain
%F jay-etal-2026-versa
%X Financial causality detection,the task of identifying and extracting verbatim causal spans from financial narratives, remains a challenging problem in Natural Language Processing (NLP). Large Language Models (LLMs), while powerful reasoners, frequently paraphrase source text or produce imprecise span boundaries when used in zero-shot extraction settings, leading to poor Exact Match scores. In this paper, we present VERSA, our system for the FinCausal 2026 Shared Task, a multi-agent pipeline that integrates two complementary inference strategies: Rephrase-and-Respond (RaR) and Recursive Self-Aggregation (RSA). The pipeline decomposes the extraction task into five sequential stages, each handled by a specialised agent: (1) causal structure analysis, (2) question reformulation via RaR, (3) diverse candidate population generation, (4) iterative refinement through RSA, and (5) verbatim validation with word-boundary alignment. We evaluate our approach on both the English and Spanish subsets of the FinCausal 2026 dataset. An ablation study demonstrates the individual and combined ontributions of RaR and RSA, showing that the full pipeline substantially outperforms a zero-shot baseline in Exact Match and token-level F1.
%R 10.63317/523w58xpfhbz
%U https://aclanthology.org/2026.fnp-1.14/
%U https://doi.org/10.63317/523w58xpfhbz
%P 139-145
Markdown (Informal)
[VERSA: Verbatim Extraction via Rephrasing and Self-Aggregation for Financial Causality](https://aclanthology.org/2026.fnp-1.14/) (Jay et al., FNP 2026)
ACL