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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, Coreference resolution, Machine translation; often with Luheng He, Mike Lewis, Ido Dagan.

OpenAlex · ORCID · h-index 93 · 82307 citations · 519 works — 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 · Semantic Scholar · dblp · ACL Anthology · ORCID

Topics: Semantic role labeling 6 · Coreference resolution 1 · Machine translation 1

Papers

  1. CREPE: Open-Domain Question Answering with False Presuppositions (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 (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 (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 (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 (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 (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 (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 (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 (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 (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)

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