6 accepted papers
Mitigating entity bias is a critical challenge in Relation Extraction (RE), where models often rely excessively on entities, resulting in poor generalization. This paper presents a novel approach to address this issue by adapting a Variational Information Bottleneck (VIB) framework. Our method compr…
We introduce FinNLI, a benchmark dataset for Financial Natural Language Inference (FinNLI) across diverse financial texts like SEC Filings, Annual Reports, and Earnings Call transcripts. Our dataset framework ensures diverse premise-hypothesis pairs while minimizing spurious correlations. FinNLI com…
Domain-specific multilingual terminology is essential for accurate machine translation (MT) and cross-lingual NLP applications. We present a gold-standard terminology resource for the tax and financial education domains, built from curated governmental publications and covering seven typologically d
Despite recent progress in automated rumour verification, little has been done on evaluating rumours in a real-world setting. We advance the state-of-the-art on the PHEME dataset, which consists of Twitter response threads collected as a rumour was unfolding. We automatically collect evidence releva…
Collecting labeled datasets in finance is challenging due to scarcity of domain experts and higher cost of employing them. While Large Language Models (LLMs) have demonstrated remarkable performance in data annotation tasks on general domain datasets, their effectiveness on domain specific datasets…
We present a comprehensive work on automated veracity assessment from dataset creation to developing novel methods based on Natural Language Inference (NLI), focusing on misinformation related to the COVID-19 pandemic. We first describe the construction of the novel PANACEA dataset consisting of het…