@inproceedings{rudra-etal-2026-c,
title = "{C}-{QUERI}: Congressional Questions, Exchanges, and Responses in Institutions Dataset",
author = "Rudra, Manjari and
Magleby, Daniel and
Sikdar, Sujoy",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2193/",
pages = "46951--46972",
ISBN = "979-8-89176-462-0",
abstract = "We introduce C-QUERI, a dataset of question-answer exchanges from U.S. congressional committee hearings spanning the 108th{--}117th Congresses, with the pipeline used to construct it. C-QUERI transforms congressional hearing records into structured question-answer exchanges, enabling researchers to study political questioning and witness responses systematically and at scale. Congressional hearings provide a particularly valuable setting for such research: interactions are structured by formal rules, members of different parties are guaranteed opportunities to question witnesses, and witnesses are expected to respond. As an initial application demonstrating the resource{'}s utility, we examine whether questioner party affiliation can be predicted from question text alone. Question text contains a modest aggregate partisan signal, with substantially greater distinguishability in some committees, hearing types, and configurations of partisan control than others. C-QUERI provides new infrastructure for studying congressional politics and a general framework for analyzing question-answer interactions in other interview-like settings."
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%0 Conference Proceedings
%T C-QUERI: Congressional Questions, Exchanges, and Responses in Institutions Dataset
%A Rudra, Manjari
%A Magleby, Daniel
%A Sikdar, Sujoy
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F rudra-etal-2026-c
%X We introduce C-QUERI, a dataset of question-answer exchanges from U.S. congressional committee hearings spanning the 108th–117th Congresses, with the pipeline used to construct it. C-QUERI transforms congressional hearing records into structured question-answer exchanges, enabling researchers to study political questioning and witness responses systematically and at scale. Congressional hearings provide a particularly valuable setting for such research: interactions are structured by formal rules, members of different parties are guaranteed opportunities to question witnesses, and witnesses are expected to respond. As an initial application demonstrating the resource’s utility, we examine whether questioner party affiliation can be predicted from question text alone. Question text contains a modest aggregate partisan signal, with substantially greater distinguishability in some committees, hearing types, and configurations of partisan control than others. C-QUERI provides new infrastructure for studying congressional politics and a general framework for analyzing question-answer interactions in other interview-like settings.
%U https://aclanthology.org/2026.findings-emnlp.2193/
%P 46951-46972
Markdown (Informal)
[C-QUERI: Congressional Questions, Exchanges, and Responses in Institutions Dataset](https://aclanthology.org/2026.findings-emnlp.2193/) (Rudra et al., Findings 2026)
ACL