ACL Computational Linguistics Doctoral Dissertation Award
The ACL Computational Linguistics Doctoral Dissertation Award (short form: ACL Dissertation Award) was established in 2024 through a proposal from the Computational Linguistics journal, as a new initiative by the ACL. This was also the year in which the Computational Linguistics journal celebrated its 50th anniversary.
The award aims to recognize and promote outstanding doctoral research in the field of computational linguistics and natural language processing. It has been presented annually at the ACL conference since 2025.
About the Award
- Recognition: Each year, one winner is selected, with the possibility of several honorable mentions.
- Prize: The winner receives a monetary prize and funding to cover travel expenses to attend the ACL conference.
- Presentation: The winner delivers an oral presentation during a special session at the ACL conference, and all awardees receive a certificate.
- Publication: The winning dissertation is published, in an adapted form, in the Computational Linguistics journal.
Eligibility and Nomination
- Each award cycle covers dissertations completed during a twelve-month window from September 1 to August 31. For example, the 2026 award covered dissertations completed between September 1, 2024 and August 31, 2025.
- Nominations are submitted by the candidate's PhD advisor and include a copy of the dissertation in English, a nomination statement (no more than 500 words) written and signed by the advisor, and up to two optional supporting letters from experts in the field.
- The nomination statement should highlight the dissertation's significance and potential long-term impact on the field, and clarify the extent to which the dissertation is the product of the student, as well as the nominee's specific contributions to any collaborative work.
- The advisor must also attest to the candidate's adherence to the ACL Code of Ethics.
- The call for nominations is typically announced on the ACL website in the autumn, with a deadline around December.
Selection Criteria
The review committee favors well-written and coherent dissertations that raise substantial novel questions relevant to ACL, investigate them through rigorous scholarship, and are worth reading in full.
Award Recipients
| Year | Award | Recipient | Dissertation | Institution |
|---|---|---|---|---|
| 2026 | Winner | Verna Dankers | Memorisation Meets Compositionality in Natural Language Processing | University of Edinburgh |
| Honorable Mention | Sheng-Chieh (Jack) Lin | Building a Robust Retrieval System with Dense Retrieval Models | University of Waterloo | |
| Honorable Mention | Dennis Ulmer | On Uncertainty in Natural Language Processing | IT University of Copenhagen | |
| Honorable Mention | Haoran Xu | Minimizing Language Interference for Multilingual Models | Johns Hopkins University | |
| 2025 | Winner | Sewon Min | Rethinking Data Use in Large Language Models | University of Washington |
| Honorable Mention | Manling Li | Event-Centric Multimodal Knowledge Acquisition | University of Illinois Urbana-Champaign | |
| Honorable Mention | Ashish Sharma | Human-AI Collaboration to Support Mental Health and Well-Being | University of Washington | |
| Honorable Mention | Tom Sherborne | Modeling Cross-lingual Transfer for Semantic Parsing | University of Edinburgh |
2026 Awards
The 2026 awards were selected from 34 nominees whose dissertations were completed during September 1, 2024 – August 31, 2025. The awards were presented during a special session at the ACL 2026 conference.
Winner
ACL is delighted to give the 2026 award to:
- Verna Dankers, Memorisation Meets Compositionality in Natural Language Processing (University of Edinburgh; advisor: Ivan Titov). Dankers' dissertation provides novel insights into how language models memorize specific training examples, such as idioms, while also capturing the compositionality of language, demonstrating how these two phenomena interact.
Honorable Mentions
ACL also recognizes three dissertations with honorable mentions (listed in alphabetical order):
- Sheng-Chieh (Jack) Lin, Building a Robust Retrieval System with Dense Retrieval Models (University of Waterloo; advisor: Jimmy Lin). Lin's dissertation contributes novel methods for information retrieval, making principled use of masked language models, advancing the state of the art while maintaining computational efficiency.
- Dennis Ulmer, On Uncertainty in Natural Language Processing (IT University of Copenhagen; advisor: Christian Hardmeier, co-advisor: Jes Frellsen). Ulmer's dissertation surveys numerous approaches to uncertainty quantification in machine learning, and applies relevant approaches to text classification, text generation, question answering, and experimental comparison of systems.
- Haoran Xu, Minimizing Language Interference for Multilingual Models (Johns Hopkins University; advisor: Kenton Murray). Xu's dissertation presents new methods to mitigate the problem of language interference in multilingual large language models, both in modeling and optimization. The ideas in this dissertation drive the machine translation systems that are actually being used, making a rare real-world impact.
Committee
Review Committee
Chair: Kathleen McKeown (Columbia)
| NAACL | EACL | AACL |
|---|---|---|
| Jason Eisner (JHU) | Andreas Vlachos (Cambridge) | Minlie Huang (Tsinghua) |
| Julia Hockenmaier (UIUC) | Isabelle Augenstein (Copenhagen) | Alice Oh (KAIST) |
Advisory/Organizing Committee
Wei Lu (EiC, Computational Linguistics), Mohit Bansal (ACL Exec)
2025 Awards
The 2025 awards were selected from 29 nominees whose dissertations were completed during September 1, 2022 – August 31, 2024. These four excellent works model different styles of dissertation in today's NLP/CL landscape. The awards were presented during a special session at the ACL 2025 conference.
Winner
ACL is delighted to give the 2025 award to:
- Sewon Min, Rethinking Data Use in Large Language Models. Min's dissertation provides key insights into the behavior and capabilities of large language models, in particular in-context learning. Its findings have impacted the core of NLP today. The dissertation was published in Computational Linguistics Volume 51, Issue 4.
Honorable Mentions
ACL also recognizes three dissertations with honorable mentions:
- Manling Li, Event-Centric Multimodal Knowledge Acquisition. Li's dissertation offers a comprehensive framework for multimodal event extraction and reasoning, advancing important tasks such as video question answering and future event prediction.
- Ashish Sharma, Human-AI Collaboration to Support Mental Health and Well-Being. Sharma's dissertation pushes the boundaries of human-AI collaboration along with research in empathy detection and generation, advancing the application of NLP to mental health.
- Tom Sherborne, Modeling Cross-lingual Transfer for Semantic Parsing. Sherborne's dissertation develops sophisticated methods for cross-lingual transfer into low-resource languages, demonstrating their effectiveness in the context of semantic parsing for integration with database APIs.
Committee
Review Committee
Chair: Kathleen McKeown (Columbia)
| NAACL | EACL | AACL |
|---|---|---|
| Jason Eisner (JHU) | Ivan Titov (Edinburgh) | Yang Liu (Tsinghua) |
| Julia Hockenmaier (UIUC) | Anna Korhonen (Cambridge) | Alice Oh (KAIST) |
Advisory/Organizing Committee
Wei Lu (EiC, Computational Linguistics), Mohit Bansal (ACL Exec)