105 citations · 147 across the 17 of their papers we have counts for
4 papers · 1 filter
BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation
Haoran Xu, Benjamin Van Durme, Kenton Murray
The success of bidirectional encoders using masked language models, such as BERT, on numerous natural language processing tasks has prompted researchers to attempt to incorporate t…
Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution
Toan Q. Nguyen, Kenton Murray, David Chiang
In this paper, we investigate the driving factors behind concatenation, a simple but effective data augmentation method for low-resource neural machine translation. Our experiments…
Joint Universal Syntactic and Semantic Parsing
Elias Stengel-Eskin, Kenton Murray, Sheng Zhang +2
While numerous attempts have been made to jointly parse syntax and semantics, high performance in one domain typically comes at the price of performance in the other. This trade-of…
Gradual Fine-Tuning for Low-Resource Domain Adaptation
Haoran Xu, Seth Ebner, Mahsa Yarmohammadi +3
Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain. Such domain adaptatio…