Benjamin Van Durme

About

Benjamin Van Durme is an Associate Professor in Computer Science and Cognitive Science at Johns Hopkins University. He is a member of the Center for Language and Speech Processing (CLSP) and leads Natural Language Understanding research at the Human Language Technology Center of Excellence (HLTCOE). His research focuses on helping people work with large amounts of information by understanding the content of documents and images, assisting in information retrieval, and enabling systems to answer questions about that content. Van Durme collaborates on research topics including natural language processing, data mining, social media analysis, machine learning, linguistic semantics, and broader areas within Artificial Intelligence and Cognitive Science. His work in decompositional semantics is organized through Decomp.io. Additionally, he serves as the research lead at Microsoft Semantic Machines.

Research topics

• Computer Science
• Artificial Intelligence
• Natural Language Processing
• Information Retrieval
• Philosophy
• History
• Art history
• Linguistics
• Geology
• Epistemology

Selected publications

• Complement Lexical Retrieval Model with Semantic Residual Embeddings

  Lecture notes in computer science · 2021 · 87 citations

  DOI

• LOME: Large Ontology Multilingual Extraction

  2021 · 24 citations

  Senior authorCorresponding

  Patrick Xia, Guanghui Qin, Siddharth Vashishtha, Yunmo Chen, Tongfei Chen, Chandler May, Craig Harman, Kyle Rawlins, Aaron Steven White, Benjamin Van Durme. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations. 2021.

  PublisherOA PDFDOI

• Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval

  2025-06-10 · 4 citations

  article

  In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text-video retrieval, our approach, Video-ColBERT, introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos. Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during trainin…

  PublisherDOI

• mmBERT: A Modern Multilingual Encoder with Annealed Language Learning

  ArXiv.org · 2025-09-08 · 2 citations

  preprintOpen accessSenior author

  Encoder-only languages models are frequently used for a variety of standard machine learning tasks, including classification and retrieval. However, there has been a lack of recent research for encoder models, especially with respect to multilingual models. We introduce mmBERT, an encoder-only language model pretrained on 3T tokens of multilingual text in over 1800 languages. To build mmBERT we introduce several novel elements, including an inverse mask ratio schedule and an inverse temperature…

  PublisherOA PDFDOI

• RE-AdaptIR: Improving Information Retrieval through Reverse Engineered Adaptation

  2025-07-13 · 1 citations

  articleOpen accessSenior author

  Large language models (LLMs) fine-tuned for text-retrieval have demonstrated state-of-the-art results across several information retrieval (IR) benchmarks. However, supervised training for improving these models requires numerous labeled examples, which are generally unavailable or expensive to acquire. In this work, we explore the effectiveness of extending reverse engineered adaptation to the context of information retrieval (RE-AdaptIR). We use RE-AdaptIR to improve LLM-based IR models using…

  PublisherOA PDFDOI

Recent grants

• Computational Statutory Reasoning

  NSF · $597k · 2022–2025

• Collaborative Research: The MegaAttitude Project: Investigating selection and polysemy at the scale of the lexicon

  NSF · $124k · 2018–2023

Frequent coauthors

• Adam Poliak

  72 shared

• Patrick Xia

  67 shared

• Catherine Havasi

  66 shared

• Felipe Meneguzzi

  65 shared

• Antoine Raux

  Honda (United States)

  65 shared

• Gita Sukthankar

  65 shared

• William F. Lawless

  Paine College

  65 shared

• Mirsad Hadžikadić

  University of North Carolina at Charlotte

  65 shared

Education

• Ph.D., Computer Science

  University of California, Berkeley

  2008

• M.S., Computer Science

  University of California, Berkeley

  2003

• B.S., Computer Science

  University of California, Berkeley

  2001

Awards & honors

• Celebrating Women in Data Science and AI Symposium recogniti…
• Amazon AI fellowship program for Johns Hopkins students

Similar researchers at Johns Hopkins University

• Colin Wilsonh-71
• Brice Ménardh-55
• Jason Eisnerh-53