---
title: "Benjamin Van Durme — papers and cards"
description: "Benjamin Van Durme: 7 paper cards in the Research library (2013–2020), with what each paper is about, the ideas Mova took from it, and the co-authors."
canonical: "https://research.elamur.ai/free/en/authors/benjamin-van-durme/"
---
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# Benjamin Van Durme

7 paper cards in the Research library · 2013–2020

Benjamin Van Durme wrote 7 of the papers Mova reads, in 2013–2020, most of them on [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/), [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/), [Universal Dependencies](https://md.research.elamur.ai/free/en/topics/universal-dependencies/); often with [Aaron Steven White](https://md.research.elamur.ai/free/en/authors/aaron-steven-white/), [Chris Callison-Burch](https://md.research.elamur.ai/free/en/authors/chris-callison-burch/), [Juri Ganitkevitch](https://md.research.elamur.ai/free/en/authors/juri-ganitkevitch/).

[OpenAlex](https://openalex.org/A5075825791) · [ORCID](https://orcid.org/0000-0003-4328-4288) · [**h-index 50**](https://openalex.org/authors/A5075825791) · [9268 citations](https://openalex.org/authors/A5075825791) · [391 works](https://openalex.org/works?filter=author.id:A5075825791) — openalex-snapshot, 2026-10-14

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Benjamin+Van+Durme) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Benjamin+Van+Durme) · [dblp](https://dblp.org/search?q=Benjamin+Van+Durme) · [ACL Anthology](https://aclanthology.org/search/?q=Benjamin+Van+Durme) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Benjamin+Van+Durme)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) 2 · [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) 2 · [Universal Dependencies](https://md.research.elamur.ai/free/en/topics/universal-dependencies/) 1 · [Semantic role labeling](https://md.research.elamur.ai/free/en/topics/semantic-role-labeling/) 1

## Ideas Mova took (3)

From the ideas ledger: an idea tried, kept or queued, or measured with no gain, and what it gave.

- **kept** — Give every frame a factuality value, not only neg and cond: happened / did not happen / not asserted (wanted, planned, hoped, asked, ordered) / uncertain (modal, hedge). *([Neural Models of Factuality](https://md.research.elamur.ai/free/en/papers/rudinger-2018-neural-models-factuality/) · implication signatures (51 verbs, prag/data/signatures.tsv) set neg/irrealis on «to» complements top-down; courses 7672/2199→7704/2206 with I2 (35db4e3f7))*
- **kept** — Let the student remember only factual frames as facts and keep the rest as wishes or plans (world/src/knowledge.rs): «The fox wanted to eat the grapes» must not answer «Did the fox eat the grapes?» with yes. *([Neural Models of Factuality](https://md.research.elamur.ai/free/en/papers/rudinger-2018-neural-models-factuality/) · the student treats an irrealis mismatch as NegDisagree; reader alone 7677/2201, with the student 7704/2206 (35db4e3f7))*
- **kept** — A realis flag on every frame: did the event happen according to the text? Mova has neg and cond; add «not realised» for the complement of want/plan/try/hope/refuse/promise, for future and modal frames, and for imperatives, so the student does not answer «Did… *([Universal Decompositional Semantics on Universal…](https://md.research.elamur.ai/free/en/papers/white-2016-universal-decompositional-semantics-universal-dependencies/) · realis flag for complements of want/plan/try… and «if» clauses kept (35db4e3f7); modals (will/would/might/may/shall/should) as irrealis dropped: −38 tests, −2…)*

## Papers

1. **[Temporal Reasoning in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/vashishtha-2020-temporal-reasoning-natural-language-inference/)** *(2020)* The paper turns four temporal datasets (TempEval3, TimeBank-Dense, RED and UDS-Time) into NLI pairs about two kinds of temporal reasoning: event order and event duration.
2. **[Incremental Neural Coreference Resolution in Constant Memory](https://md.research.elamur.ai/free/en/papers/xia-2020-incremental-neural-coreference-resolution-constant/)** *(2020)* The paper turns a strong all-pairs coreference model (SpanBERT, Joshi et al. 2020) into an incremental one that reads a document segment by segment and keeps a fixed-size list of entities. Each new mention is scored against entity representations (not individual earlier mentions); it joins the best entity if the…
3. **[Neural Models of Factuality](https://md.research.elamur.ai/free/en/papers/rudinger-2018-neural-models-factuality/)** *(2018)* `2 ideas kept` Event factuality asks, for each event-denoting predicate, whether the event happened according to the author, on a scale from −3 (certainly not) to +3 (certainly did).
4. **[Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework](https://md.research.elamur.ai/free/en/papers/white-2017-inference-is-everything-recasting-semantic/)** *(2017)* The paper turns existing semantic annotations into textual-inference (RTE) pairs with templates. Three resources are recast: semantic proto-roles (SPR, about 155k pairs), FrameNet paraphrases (FN+, about 155k) and definite pronoun resolution (DPR, about 3.7k). Each pair is labelled entailed or not from the original…
5. **[Universal Decompositional Semantics on Universal Dependencies](https://md.research.elamur.ai/free/en/papers/white-2016-universal-decompositional-semantics-universal-dependencies/)** *(2016)* `1 idea kept` The Decomp project adds semantic annotation on top of UD treebanks by asking ordinary speakers simple, decomposed questions with Likert-scale answers instead of asking for labels like Agent.
6. **[PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification](https://md.research.elamur.ai/free/en/papers/pavlick-2015-ppdb-2-0-better-paraphrase/)** *(2015)* PPDB 1.0 ranked its pivot-extracted paraphrases with a fixed heuristic, and this paper replaces the ranking with a discriminative model.
7. **[PPDB: The Paraphrase Database](https://md.research.elamur.ai/free/en/papers/ganitkevitch-2013-ppdb-paraphrase-database/)** *(2013)* The paper releases PPDB 1.0, a large collection of ranked paraphrases extracted from bilingual parallel corpora by pivoting (the method of Bannard and Callison-Burch).

## Co-authors (17)

- [Aaron Steven White](https://md.research.elamur.ai/free/en/authors/aaron-steven-white/) ×4
- [Chris Callison-Burch](https://md.research.elamur.ai/free/en/authors/chris-callison-burch/) ×2
- [Juri Ganitkevitch](https://md.research.elamur.ai/free/en/authors/juri-ganitkevitch/) ×2
- [Pushpendre Rastogi](https://md.research.elamur.ai/free/en/authors/pushpendre-rastogi/) ×2
- [Rachel Rudinger](https://md.research.elamur.ai/free/en/authors/rachel-rudinger/) ×2
- Adam Poliak
- Drew Reisinger
- [Ellie Pavlick](https://md.research.elamur.ai/free/en/authors/ellie-pavlick/)
- João Sedoc
- [Keisuke Sakaguchi](https://md.research.elamur.ai/free/en/authors/keisuke-sakaguchi/)
- [Kevin Duh](https://md.research.elamur.ai/free/en/authors/kevin-duh/)
- Kyle Rawlins
- [Patrick Xia](https://md.research.elamur.ai/free/en/authors/patrick-xia/)
- Sheng Zhang
- Siddharth Vashishtha
- Tim Vieira
- [Yash Kumar Lal](https://md.research.elamur.ai/free/en/authors/yash-kumar-lal/)

## More papers in the library (19)

Pages of the Research library: our summary, the ideas, references and who cites each.

- [Synthetic Function Demonstrations Improve Generation in Low-Resource Programming Languages](https://md.research.elamur.ai/free/en/papers/mckenna-2026-synthetic-function-demonstrations-improve-generation/) — Nick McKenna, Xinnuo Xu, Jack Williams, et al. · 2026
- [Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation](https://md.research.elamur.ai/free/en/papers/xu-2024-contrastive-preference-optimization-pushing-boundaries/) — Haoran Xu, Amr Sharaf, Yunmo Chen, et al. · 2024
- [Can GPT-3 Perform Statutory Reasoning?](https://md.research.elamur.ai/free/en/papers/blair-2023-stanek-gpt-3-perform-statutory-reasoning/) — Andrew Blair-Stanek, Nils Holzenberger, Benjamin Van Durme · 2023
- [Multilingual Coreference Resolution in Multiparty Dialogue](https://md.research.elamur.ai/free/en/papers/zheng-2023-multilingual-coreference-resolution-multiparty-dialogue/) — Boyuan Zheng, Patrick Xia, Mahsa Yarmohammadi, et al. · 2023
- [Adapting Coreference Resolution Models through Active Learning](https://md.research.elamur.ai/free/en/papers/yuan-2022-adapting-coreference-resolution-models-through/) — Michelle Yuan, Patrick Xia, Chandler May, et al. · 2022
- [Joint Universal Syntactic and Semantic Parsing](https://md.research.elamur.ai/free/en/papers/eskin-2021-joint-universal-syntactic-semantic-parsing/) — Elias Stengel-Eskin, Kenton Murray, Sheng Zhang, et al. · 2021
- [Multi-Sentence Argument Linking](https://md.research.elamur.ai/free/en/papers/ebner-2020-multi-sentence-argument-linking/) — Seth Ebner, Patrick Xia, Ryan Culkin, et al. · 2020
- [Uncertain Natural Language Inference](https://md.research.elamur.ai/free/en/papers/chen-2020-uncertain-natural-language-inference/) — Tongfei Chen, Zhengping Jiang, Adam Poliak, et al. · 2020
- [Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/belinkov-2019-don-t-take-premise-granted/) — Yonatan Belinkov, Adam Poliak, Stuart Shieber, et al. · 2019
- [Probing What Different NLP Tasks Teach Machines about Function Word Comprehension](https://md.research.elamur.ai/free/en/papers/kim-2019-probing-what-different-nlp-tasks/) — Najoung Kim, Roma Patel, Adam Poliak, et al. · 2019
- [What do you learn from context? Probing for sentence structure in contextualized word representations](https://md.research.elamur.ai/free/en/papers/tenney-2019-learn-context-probing-sentence-structure/) — Ian Tenney, Patrick Xia, Berlin Chen, et al. · 2019
- [Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation](https://md.research.elamur.ai/free/en/papers/poliak-2018-collecting-diverse-natural-language-inference/) — Adam Poliak, Aparajita Haldar, Rachel Rudinger, et al. · 2018
- [Gender Bias in Coreference Resolution](https://md.research.elamur.ai/free/en/papers/rudinger-2018-gender-bias-coreference-resolution/) — Rachel Rudinger, Jason Naradowsky, Brian Leonard, et al. · 2018
- [ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension](https://md.research.elamur.ai/free/en/papers/zhang-2018-record-bridging-gap-between-human/) — Sheng Zhang, Xiaodong Liu, Jingjing Liu, et al. · 2018
- [Ordinal Common-sense Inference](https://md.research.elamur.ai/free/en/papers/zhang-2017-ordinal-common-sense-inference/) — Sheng Zhang, Rachel Rudinger, Kevin Duh, et al. · 2017
- [Social Bias in Elicited Natural Language Inferences](https://md.research.elamur.ai/free/en/papers/rudinger-2017-social-bias-elicited-natural-language/) — Rachel Rudinger, Chandler May, Benjamin Van Durme · 2017
- [FrameNet+: Fast Paraphrastic Tripling of FrameNet](https://md.research.elamur.ai/free/en/papers/pavlick-2015-framenet-fast-paraphrastic-tripling-framenet/) — Ellie Pavlick, Travis Wolfe, Pushpendre Rastogi, et al. · 2015
- [A Comparison of the Events and Relations Across ACE, ERE, TAC-KBP, and FrameNet Annotation Standards](https://md.research.elamur.ai/free/en/papers/aguilar-2014-comparison-events-relations-across-ace/) — Jacqueline Aguilar, Charley Beller, Paul McNamee, et al. · 2014
- [Augmenting FrameNet Via PPDB](https://md.research.elamur.ai/free/en/papers/rastogi-2014-augmenting-framenet-ppdb/) — Pushpendre Rastogi, Benjamin Van Durme · 2014