343 citations · 396 across the 8 of their papers we have counts for
7 papers
Improving Word Sense Disambiguation in Neural Machine Translation with Salient Document Context
Elijah Rippeth, Marine Carpuat, Kevin Duh +1
Lexical ambiguity is a challenging and pervasive problem in machine translation (\mt). We introduce a simple and scalable approach to resolve translation ambiguity by incorporating…
A Survey of Vision-Language Pre-training from the Lens of Multimodal Machine Translation
Jeremy Gwinnup, Kevin Duh
Large language models such as BERT and the GPT series started a paradigm shift that calls for building general-purpose models via pre-training on large datasets, followed by fine-t…
In-context Learning as Maintaining Coherency: A Study of On-the-fly Machine Translation Using Large Language Models
Suzanna Sia, Kevin Duh
The phenomena of in-context learning has typically been thought of as "learning from examples". In this work which focuses on Machine Translation, we present a perspective of in-co…
DyNet: The Dynamic Neural Network Toolkit
Graham Neubig, Chris Dyer, Yoav Goldberg +22
We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits l…
Robsut Wrod Reocginiton via semi-Character Recurrent Neural Network
Keisuke Sakaguchi, Kevin Duh, Matt Post +1
Language processing mechanism by humans is generally more robust than computers. The Cmabrigde Uinervtisy (Cambridge University) effect from the psycholinguistics literature has de…
Incorporating Both Distributional and Relational Semantics in Word Representations
Daniel Fried, Kevin Duh
We investigate the hypothesis that word representations ought to incorporate both distributional and relational semantics. To this end, we employ the Alternating Direction Method o…