@inproceedings{kannan-etal-2026-argus,
title = "{ARGUS}: Role-Aware Event Knowledge Graphs for {U}.{S}. Employment-Discrimination Complaints",
author = "Kannan, Sriram and
Saseendran, Swetha and
Kandi, Vishnu Vardhan Reddy and
Barrett, Leslie and
Seshadri, Madhavan and
Santus, Enrico",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goanț{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preoțiuc-Pietro, Daniel and
Spanakis, Gerasimos and
Xu, Shanshan",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nllp-1.14/",
pages = "142--155",
ISBN = "979-8-89176-494-1",
abstract = "U.S. employment-discrimination complaints describe complex event sequences that are not explicitly captured by lexical or embedding-based representations alone. We present ARGUS, a source-grounded pipeline that combines a 5W1H-inspired schema, legal-domain models, and LLM-based structured generation to construct document-level Event Knowledge Graphs (EKGs) from CourtListener complaints. ARGUS extracts fact-bearing statements, builds chunk-level event graphs with participant, temporal, and causal structure, and merges them into document-level representations. We evaluate graph quality through human and multi-model assessment and test downstream utility on claim classification and legal QA. The graph-structured classifier outperforms raw and linearized baselines on the held-out set, and EKG-only retrieval improves document-scoped QA, while open-retrieval gains remain limited by low first-stage candidate recall. These results suggest that EKGs are most useful for organizing and reasoning over evidence once relevant material has been retrieved."
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%0 Conference Proceedings
%T ARGUS: Role-Aware Event Knowledge Graphs for U.S. Employment-Discrimination Complaints
%A Kannan, Sriram
%A Saseendran, Swetha
%A Kandi, Vishnu Vardhan Reddy
%A Barrett, Leslie
%A Seshadri, Madhavan
%A Santus, Enrico
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%Y Xu, Shanshan
%S Proceedings of the Natural Legal Language Processing Workshop 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-494-1
%F kannan-etal-2026-argus
%X U.S. employment-discrimination complaints describe complex event sequences that are not explicitly captured by lexical or embedding-based representations alone. We present ARGUS, a source-grounded pipeline that combines a 5W1H-inspired schema, legal-domain models, and LLM-based structured generation to construct document-level Event Knowledge Graphs (EKGs) from CourtListener complaints. ARGUS extracts fact-bearing statements, builds chunk-level event graphs with participant, temporal, and causal structure, and merges them into document-level representations. We evaluate graph quality through human and multi-model assessment and test downstream utility on claim classification and legal QA. The graph-structured classifier outperforms raw and linearized baselines on the held-out set, and EKG-only retrieval improves document-scoped QA, while open-retrieval gains remain limited by low first-stage candidate recall. These results suggest that EKGs are most useful for organizing and reasoning over evidence once relevant material has been retrieved.
%U https://aclanthology.org/2026.nllp-1.14/
%P 142-155
Markdown (Informal)
[ARGUS: Role-Aware Event Knowledge Graphs for U.S. Employment-Discrimination Complaints](https://aclanthology.org/2026.nllp-1.14/) (Kannan et al., NLLP 2026)
ACL