Computer Science > Computation and Language
arXiv:2403.14341 (cs)
[Submitted on 21 Mar 2024]
Title:Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narratives
View a PDF of the paper titled Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narratives, by Jiaxin Liu and Yi Yang and Kar Yan Tam
View PDFHTML (experimental)Abstract:In this paper, we introduce the Financial-STS task, a financial domain-specific NLP task designed to measure the nuanced semantic similarity between pairs of financial narratives. These narratives originate from the financial statements of the same company but correspond to different periods, such as year-over-year comparisons. Measuring the subtle semantic differences between these paired narratives enables market stakeholders to gauge changes over time in the company's financial and operational situations, which is critical for financial decision-making. We find that existing pretrained embedding models and LLM embeddings fall short in discerning these subtle financial narrative shifts. To address this gap, we propose an LLM-augmented pipeline specifically designed for the Financial-STS task. Evaluation on a human-annotated dataset demonstrates that our proposed method outperforms existing methods trained on classic STS tasks and generic LLM embeddings.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2403.14341 [cs.CL] |
| (orarXiv:2403.14341v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2403.14341 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narratives, by Jiaxin Liu and Yi Yang and Kar Yan Tam
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