K
Kevin Duh
Researcher at Johns Hopkins University
Publications - 205
Citations - 6391
Kevin Duh is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Machine translation & Parsing. The author has an hindex of 38, co-authored 205 publications receiving 5369 citations. Previous affiliations of Kevin Duh include University of Washington & Nara Institute of Science and Technology.
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AMR Parsing as Sequence-to-Graph Transduction.
TL;DR: This work proposes an attention-based model that treats AMR parsing as sequence-to-graph transduction, and it can be effectively trained with limited amounts of labeled AMR data.
Proceedings Article
Head Finalization: A Simple Reordering Rule for SOV Languages
TL;DR: This paper proposes an alternative single reordering rule: Head Finalization, a syntax-based preprocessing approach that offers the advantage of simplicity and shows that its result, Head Final English (HFE), follows almost the same order as Japanese.
Posted Content
Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning
TL;DR: It is concluded that BERT can be pruned once during pre-training rather than separately for each task without affecting performance, and that fine-tuning BERT on a specific task does not improve its prunability.
Proceedings Article
Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework
TL;DR: A general strategy to automatically generate one or more sentential hypotheses based on an input sentence and pre-existing manual semantic annotations is presented, which enables us to probe a statistical RTE model’s performance on different aspects of semantics.
Posted Content
An Empirical Exploration of Curriculum Learning for Neural Machine Translation
Xuan Zhang,Gaurav Kumar,Huda Khayrallah,Kenton Murray,Jeremy Gwinnup,Marianna J. Martindale,Paul McNamee,Kevin Duh,Marine Carpuat +8 more
TL;DR: A probabilistic view of curriculum learning is adopted, which lets us flexibly evaluate the impact of curricula design, and an extensive exploration on a German-English translation task shows it is possible to improve convergence time at no loss in translation quality.