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 "name": "CoCoHD_transcripts",
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 "citation": "@inproceedings{hiray-etal-2024-cocohd,\n    title = \"{C}o{C}o{HD}: Congress Committee Hearing Dataset\",\n    author = \"Hiray, Arnav  and\n      Liu, Yunsong  and\n      Song, Mingxiao  and\n      Shah, Agam  and\n      Chava, Sudheer\",\n    editor = \"Al-Onaizan, Yaser  and\n      Bansal, Mohit  and\n      Chen, Yun-Nung\",\n    booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2024\",\n    month = nov,\n    year = \"2024\",\n    address = \"Miami, Florida, USA\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2024.findings-emnlp.911\",\n    doi = \"10.18653/v1/2024.findings-emnlp.911\",\n    pages = \"15529--15542\",\n    abstract = \"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 sho",
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