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Luke Zettlemoyer
Researcher at Facebook
Publications - 344
Citations - 65369
Luke Zettlemoyer is an academic researcher from Facebook. The author has contributed to research in topics: Computer science & Parsing. The author has an hindex of 82, co-authored 278 publications receiving 40896 citations. Previous affiliations of Luke Zettlemoyer include Princeton University & Massachusetts Institute of Technology.
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Proceedings ArticleDOI
Grounded Adaptation for Zero-shot Executable Semantic Parsing
TL;DR: The authors propose Grounded Adaptation for Zeroshot Executable Semantic Parsing (GAZP), which combines a forward semantic parser with a backward utterance generator to synthesize data (e.g. utterances and SQL queries) in the new environment, then selects cycle-consistent examples to adapt the parser.
Proceedings Article
Better Fine-Tuning by Reducing Representational Collapse
TL;DR: In this article, a simplified and efficient method rooted in trust region theory that replaces previously used adversarial objectives with parametric noise (sampling from either a normal or uniform distribution), thereby discouraging representation change during fine-tuning when possible without hurting performance.
Proceedings ArticleDOI
JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation
TL;DR: To study code generation conditioned on a long context history, JuICe is presented, a corpus of 1.5 million examples with a curated test set of 3.7K instances based on online programming assignments that provides refined human-curated data, open-domain code, and an order of magnitude more training data.
Proceedings ArticleDOI
pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference.
TL;DR: The authors propose to learn word embeddings by maximizing the pointwise mutual information (PMI) with the contexts in which the two words co-occur and add these representations to the cross-sentence attention layer of existing inference models.
Journal ArticleDOI
3D Wikipedia: using online text to automatically label and navigate reconstructed geometry
TL;DR: This work introduces an approach for analyzing Wikipedia and other text, together with online photos, to produce annotated 3D models of famous tourist sites, which leverages online text and photo co-occurrences via Google Image Search.