[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/authors/benjamin-van-durme/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/) / Benjamin Van Durme # Benjamin Van Durme 7 paper cards in Mova's library · 2013–2020 Benjamin Van Durme wrote 7 of the papers Mova reads, in 2013–2020, most of them on [Coreference resolution](https://md.mova.elamur.ai/free/en/docs/papers/topics/coreference-resolution/), [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/), [Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/papers/topics/universal-dependencies/); often with [Aaron Steven White](https://md.mova.elamur.ai/free/en/docs/papers/authors/aaron-steven-white/), [Chris Callison-Burch](https://md.mova.elamur.ai/free/en/docs/papers/authors/chris-callison-burch/), [Juri Ganitkevitch](https://md.mova.elamur.ai/free/en/docs/papers/authors/juri-ganitkevitch/). [OpenAlex](https://openalex.org/A5075825791) · [ORCID](https://orcid.org/0000-0003-4328-4288) · [**h-index 51**](https://openalex.org/authors/A5075825791) · [9972 citations](https://openalex.org/authors/A5075825791) · [392 works](https://openalex.org/works?filter=author.id:A5075825791) — openalex, 2026-10-18 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.mova.elamur.ai/free/en/docs/papers/topics/coreference-resolution/) 2 · [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 2 · [Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/papers/topics/universal-dependencies/) 1 · [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/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.mova.elamur.ai/free/en/docs/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.mova.elamur.ai/free/en/docs/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.mova.elamur.ai/free/en/docs/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.mova.elamur.ai/free/en/docs/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.mova.elamur.ai/free/en/docs/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.mova.elamur.ai/free/en/docs/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.mova.elamur.ai/free/en/docs/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.mova.elamur.ai/free/en/docs/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.mova.elamur.ai/free/en/docs/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.mova.elamur.ai/free/en/docs/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.mova.elamur.ai/free/en/docs/papers/authors/aaron-steven-white/) ×4 - [Chris Callison-Burch](https://md.mova.elamur.ai/free/en/docs/papers/authors/chris-callison-burch/) ×2 - [Juri Ganitkevitch](https://md.mova.elamur.ai/free/en/docs/papers/authors/juri-ganitkevitch/) ×2 - [Pushpendre Rastogi](https://md.mova.elamur.ai/free/en/docs/papers/authors/pushpendre-rastogi/) ×2 - [Rachel Rudinger](https://md.mova.elamur.ai/free/en/docs/papers/authors/rachel-rudinger/) ×2 - Adam Poliak - Drew Reisinger - [Ellie Pavlick](https://md.mova.elamur.ai/free/en/docs/papers/authors/ellie-pavlick/) - João Sedoc - [Keisuke Sakaguchi](https://md.mova.elamur.ai/free/en/docs/papers/authors/keisuke-sakaguchi/) - [Kevin Duh](https://md.mova.elamur.ai/free/en/docs/papers/authors/kevin-duh/) - Kyle Rawlins - [Patrick Xia](https://md.mova.elamur.ai/free/en/docs/papers/authors/patrick-xia/) - Sheng Zhang - Siddharth Vashishtha - Tim Vieira - [Yash Kumar Lal](https://md.mova.elamur.ai/free/en/docs/papers/authors/yash-kumar-lal/) - [← previous Beatrice Santorini](https://md.mova.elamur.ai/free/en/docs/papers/authors/beatrice-santorini/) - [next → Benoit Lavoie](https://md.mova.elamur.ai/free/en/docs/papers/authors/benoit-lavoie/) - [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)