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Steven H. Wang

dblp:296/4286 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%
Artificial intelligence
1 paper
Information extraction and text analysis · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › retrieval models › neural retrieval › dense retrieval
bi-encoder retrieval
0.912025
ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Drafting · ACL (1) 2025
Information retrieval › evaluation › test collection
retrieval benchmark
0.912025
ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Drafting · ACL (1) 2025
Information retrieval
retrieval models
0.912025
ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Drafting · ACL (1) 2025
Natural language and speech › Information extraction and text analysis
dataset construction
0.712023
MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding · EMNLP 2023
Natural language and speech › Information extraction and text analysis › document understanding
legal text analysis
0.712023
MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding · EMNLP 2023
Information retrieval
retrieval evaluation
0.312025
ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Drafting · ACL (1) 2025
Computational social science and digital humanities
legal informatics
0.212023
MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding · EMNLP 2023

Methods — techniques the papers use, named apart from their topics

expert annotation · 1.3pointwise ranking · 0.9bi-encoder retriever · 0.9LLM re-ranker · 0.9
YearPublicationVenuePosition
2025 ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Drafting
abstract
Contract clause retrieval is foundational to contract drafting because lawyers rarely draft contracts from scratch; instead, they locate and revise the most relevant precedent clauses. We introduce the Atticus Clause Retrieval Dataset (ACORD), the first expert-annotated benchmark specifically designed for contract clause retrieval to support contract drafting tasks. ACORD focuses on complex contract clauses such as Limitation of Liability, Indemnification, Change of Control, and Most Favored Nation. It includes 114 queries and over 126,000 query-clause pairs, each ranked on a scale from 1 to 5 stars. The task is to find the most relevant precedent clauses to a query. The bi-encoder retriever paired with pointwise LLMs re-rankers shows promising results. However, substantial improvements are still needed to manage the complex legal work typically undertaken by lawyers effectively. As the first expert-annotated benchmark for contract clause retrieval, ACORD can serve as a valuable IR benchmark for the NLP community.
Steven H. Wang, Maksim Zubkov, Kexin Fan, Sarah Harrell, Andreas Plesner, Roger Wattenhofer
ACL (1)1
2023 MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding
abstract
Steven Wang, Antoine Scardigli, Leonard Tang, Wei Chen, Dmitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, Dan Hendrycks. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
Steven H. Wang, Antoine Scardigli, Leonard Tang, Dimitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, Dan Hendrycks
EMNLP1