L
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.
Papers
More filters
Proceedings ArticleDOI
The Referential Reader: A Recurrent Entity Network for Anaphora Resolution
TL;DR: In this article, a new architecture for storing and accessing entity mentions during online text processing is presented, where references are identified, and may be stored by either updating or overwriting a cell in a fixed-length memory.
Posted Content
Enforcing Encoder-Decoder Modularity in Sequence-to-Sequence Models
TL;DR: This work introduces a method for enforcing encoder-decoder modularity in seq2seq models without sacrificing the overall model quality or its full differentiability, and discretizes the encoder output units into a predefined interpretable vocabulary space using the Connectionist Temporal Classification (CTC) loss.
Posted Content
Extreme Extraction: Only One Hour per Relation
TL;DR: A novel system is presented, InstaRead, that streamlines authoring with an ensemble of methods: encoding extraction rules in an expressive and compositional representation, guiding the user to promising rules based on corpus statistics and mined resources, and introducing a new interactive development cycle that provides immediate feedback --- even on large datasets.
Posted Content
Higher-order Coreference Resolution with Coarse-to-fine Inference
TL;DR: The authors use the antecedent distribution from a span-ranking architecture as an attention mechanism to iteratively refine span representations, which enables the model to softly consider multiple hops in the predicted clusters.
Posted Content
Controlled Crowdsourcing for High-Quality QA-SRL Annotation
Paul Roit,Ayal Klein,Daniela Stepanov,Jonathan Mamou,Julian Michael,Gabriel Stanovsky,Luke Zettlemoyer,Ido Dagan +7 more
TL;DR: An improved crowdsourcing protocol for complex semantic annotation, involving worker selection and training, and a data consolidation phase is presented, which yielded high-quality annotation with drastically higher coverage, producing a new gold evaluation dataset.