Arnav Hiray


2024

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CoCoHD: Congress Committee Hearing Dataset
Arnav Hiray | Yunsong Liu | Mingxiao Song | Agam Shah | Sudheer Chava
Findings of the Association for Computational Linguistics: EMNLP 2024

U.S. congressional hearings significantly influence the national economy and social fabric, impacting individual lives. Despite their importance, there is a lack of comprehensive datasets for analyzing these discourses. To address this, we propose the **Co**ngress **Co**mmittee **H**earing **D**ataset (CoCoHD), covering hearings from 1997 to 2024 across 86 committees, with 32,697 records. This dataset enables researchers to study policy language on critical issues like healthcare, LGBTQ+ rights, and climate justice. We demonstrate its potential with a case study on 1,000 energy-related sentences, analyzing the Energy and Commerce Committee’s stance on fossil fuel consumption. By fine-tuning pre-trained language models, we create energy-relevant measures for each hearing. Our market analysis shows that natural language analysis using CoCoHD can predict and highlight trends in the energy sector.

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Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis
Agam Shah | Arnav Hiray | Pratvi Shah | Arkaprabha Banerjee | Anushka Singh | Dheeraj Deepak Eidnani | Sahasra Chava | Bhaskar Chaudhury | Sudheer Chava
Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)

In this paper, we investigate the influence of claims in analyst reports and earnings calls on financial market returns, considering them as significant quarterly events for publicly traded companies. To facilitate a comprehensive analysis, we construct a new financial dataset for the claim detection task in the financial domain. We benchmark various language models on this dataset and propose a novel weak-supervision model that incorporates the knowledge of subject matter experts (SMEs) in the aggregation function, outperforming existing approaches. We also demonstrate the practical utility of our proposed model by constructing a novel measure of *optimism*. Here, we observe the dependence of earnings surprise and return on our optimism measure. Our dataset, models, and code are publicly (under CC BY 4.0 license) available on GitHub.