Long documents such as academic articles and business reports have been the standard
format to detail out important issues and complicated subjects that require extra attention. An …

The new era of technology has brought us to the point where it is convenient for people to
share their opinions over an abundance of platforms. These platforms have a provision for …

Monetary policy pronouncements by Federal Open Market Committee (FOMC) are a major
driver of financial market returns. We construct the largest tokenized and annotated dataset …

While state-of-the-art NLP models have been achieving the excellent performance of a wide
range of tasks in recent years, important questions are being raised about their robustness …

We present a novel rationale-centric framework with human-in-the-loop--Rationales-centric
Double-robustness Learning (RDL)--to boost model out-of-distribution performance in few …

Financial forecasting has been an important and active area of machine learning research
because of the challenges it presents and the potential rewards that even minor …

Large Language Models (LLMs) have shown remarkable capabilities across a wide variety
of Natural Language Processing (NLP) tasks and have attracted attention from multiple …

This paper addresses the task of predicting the occurrence of corporate events based on
earning call transcripts. We introduce a novel dataset of earning call transcripts specifically …

Stock volatility prediction is an important task in the financial industry. Recent advancements
in multimodal methodologies, which integrate both textual and auditory data, have …

Predicting the bankruptcy risk of small and medium-sized enterprises (SMEs) is crucial for
making decisions about loans. Existing studies in both finance and AI research fields …