[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/authors/luke-zettlemoyer/) [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/) / Luke Zettlemoyer # Luke Zettlemoyer 10 paper cards in Mova's library · 2015–2023 Luke Zettlemoyer wrote 10 of the papers Mova reads, in 2015–2023, most of them on [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/topics/semantic-role-labeling/), [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/); often with [Luheng He](https://md.mova.elamur.ai/free/en/docs/papers/authors/luheng-he/), [Mike Lewis](https://md.mova.elamur.ai/free/en/docs/papers/authors/mike-lewis/), [Ido Dagan](https://md.mova.elamur.ai/free/en/docs/papers/authors/ido-dagan/). [OpenAlex](https://openalex.org/A5067919401) · [ORCID](https://orcid.org/0009-0008-8296-0764) · [**h-index 93**](https://openalex.org/authors/A5067919401) · [82307 citations](https://openalex.org/authors/A5067919401) · [519 works](https://openalex.org/works?filter=author.id:A5067919401) — 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=Luke+Zettlemoyer) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Luke+Zettlemoyer) · [dblp](https://dblp.org/search?q=Luke+Zettlemoyer) · [ACL Anthology](https://aclanthology.org/search/?q=Luke+Zettlemoyer) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Luke+Zettlemoyer) Topics: [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/topics/semantic-role-labeling/) 6 · [Coreference resolution](https://md.mova.elamur.ai/free/en/docs/papers/topics/coreference-resolution/) 1 · [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 1 ## Papers 1. **[CREPE: Open-Domain Question Answering with False Presuppositions](https://md.mova.elamur.ai/free/en/docs/papers/yu-2023-crepe-open-domain-question-answering/)** *(2023)* CREPE has 8,400 «explain like I'm five» questions from Reddit. Annotators, guided by the most upvoted answer, mark whether the question rests on a false presupposition and, if so, write the presupposition and a correction. About 25% of real information-seeking questions contain one. A sample of 50 shows the kinds: a… 2. **[Controlled Crowdsourcing for High-Quality QA-SRL Annotation](https://md.mova.elamur.ai/free/en/docs/papers/roit-2020-controlled-crowdsourcing-high-quality-qa/)** *(2020)* The paper shows that the crowdsourced QA-SRL data of 2018 has low coverage, with an estimated recall in the low 70s, and proposes a controlled protocol to fix it. 3. **[QANom: Question-Answer driven SRL for Nominalizations](https://md.mova.elamur.ai/free/en/docs/papers/klein-2020-qanom-question-answer-driven-srl/)** *(2020)* QANom extends QA-SRL from verbs to nouns derived from verbs: a noun like proof or increase is questioned with its verb's question templates («What has someone proved?», «Why did something increase?») and answered with spans of the sentence. 4. **[Large-Scale QA-SRL Parsing](https://md.mova.elamur.ai/free/en/docs/papers/fitzgerald-2018-large-scale-qa-srl-parsing/)** *(2018)* The paper releases QA-SRL Bank 2.0, which has 265,140 valid question-answer pairs for 64,018 sentences and 133,479 verbs across Wikipedia, Wikinews and science textbooks. 5. **[Crowdsourcing Question-Answer Meaning Representations](https://md.mova.elamur.ai/free/en/docs/papers/michael-2018-crowdsourcing-question-answer-meaning-representations/)** *(2018)* A QAMR represents a sentence's predicate-argument structure as a set of free-form wh-question / answer-span pairs about all its content words, not only verbs («How old is Pierre Vinken? – 61 years old», «What will he join the board as? – nonexecutive director»). 6. **[End-to-end Neural Coreference Resolution](https://md.mova.elamur.ai/free/en/docs/papers/lee-2017-end-end-neural-coreference-resolution/)** *(2017)* The first coreference model trained end to end without a parser or a mention detector. Every span up to 10 words is a candidate; each span gets a mention score and each pair an antecedent score, and a span chooses among all earlier spans plus a dummy antecedent fixed at score 0 (no link, or not a mention). Span… 7. **[Deep Semantic Role Labeling: What Works and What's Next](https://md.mova.elamur.ai/free/en/docs/papers/he-2017-deep-semantic-role-labeling-what/)** *(2017)* A neural SRL model that treats each predicate's arguments as BIO tags over the sentence, with no syntax input. 8. **[Neural AMR: Sequence-to-Sequence Models for Parsing and Generation](https://md.mova.elamur.ai/free/en/docs/papers/konstas-2017-neural-amr-sequence-sequence-models/)** *(2017)* The paper applies sequence-to-sequence neural models to both AMR parsing and AMR generation. The main obstacle is data sparsity, so the authors pretrain on millions of unlabelled Gigaword sentences: a parser labels them, a generator is pretrained on those pairs, and then both are fine-tuned on the 16,833 gold pairs.… 9. **[LSTM CCG Parsing](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2016-lewis-lstm-ccg-parsing/)** *(2016)* The paper asks whether a CCG parser needs an explicit model of bilexical dependencies. Its parser has none: a bidirectional LSTM supertagger assigns lexical categories, a deterministic grammar combines them, and A* search finds the best parse. Semi-supervised tri-training adds training signal. On CCGbank section 23… 10. **[Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language](https://md.mova.elamur.ai/free/en/docs/papers/he-2015-question-answer-driven-semantic-role/)** *(2015)* The paper introduces question-answer driven semantic role labeling (QA-SRL): each verbal predicate is labeled with wh-questions, such as "Who finished something?", and each question is answered by phrases of the sentence. ## Co-authors (21) - [Luheng He](https://md.mova.elamur.ai/free/en/docs/papers/authors/luheng-he/) ×5 - [Mike Lewis](https://md.mova.elamur.ai/free/en/docs/papers/authors/mike-lewis/) ×4 - [Ido Dagan](https://md.mova.elamur.ai/free/en/docs/papers/authors/ido-dagan/) ×3 - [Julian Michael](https://md.mova.elamur.ai/free/en/docs/papers/authors/julian-michael/) ×3 - [Kenton Lee](https://md.mova.elamur.ai/free/en/docs/papers/authors/kenton-lee/) ×3 - [Ayal Klein](https://md.mova.elamur.ai/free/en/docs/papers/authors/ayal-klein/) ×2 - [Daniela Stepanov](https://md.mova.elamur.ai/free/en/docs/papers/authors/daniela-stepanov/) ×2 - [Gabriel Stanovsky](https://md.mova.elamur.ai/free/en/docs/papers/authors/gabriel-stanovsky/) ×2 - [Jonathan Mamou](https://md.mova.elamur.ai/free/en/docs/papers/authors/jonathan-mamou/) ×2 - [Dan Roth](https://md.mova.elamur.ai/free/en/docs/papers/authors/dan-roth/) - Hangfeng He - [Hannaneh Hajishirzi](https://md.mova.elamur.ai/free/en/docs/papers/authors/hannaneh-hajishirzi/) - Ioannis Konstas - [Mark Yatskar](https://md.mova.elamur.ai/free/en/docs/papers/authors/mark-yatskar/) - Nicholas FitzGerald - Paul Roit - Sewon Min - Srinivasan Iyer - [Valentina Pyatkin](https://md.mova.elamur.ai/free/en/docs/papers/authors/valentina-pyatkin/) - Xinyan Yu - [Yejin Choi](https://md.mova.elamur.ai/free/en/docs/papers/authors/yejin-choi/) - [← previous Luke Gessler](https://md.mova.elamur.ai/free/en/docs/papers/authors/luke-gessler/) - [next → Lynn Carlson](https://md.mova.elamur.ai/free/en/docs/papers/authors/lynn-carlson/) - [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)