24 citations · 24 across the 1 of their papers we have counts for
3 papers
cs.LG2020★ 24 cited
When Ensembling Smaller Models is More Efficient than Single Large Models
Dan Kondratyuk, Mingxing Tan, Matthew Brown +1
Ensembling is a simple and popular technique for boosting evaluation performance by training multiple models (e.g., with different initializations) and aggregating their prediction…
cs.CL2019
75 Languages, 1 Model: Parsing Universal Dependencies Universally
Dan Kondratyuk, Milan Straka
We present UDify, a multilingual multi-task model capable of accurately predicting universal part-of-speech, morphological features, lemmas, and dependency trees simultaneously for…
cs.CL2018
LemmaTag: Jointly Tagging and Lemmatizing for Morphologically-Rich Languages with BRNNs
Daniel Kondratyuk, Tomáš Gavenčiak, Milan Straka +1
We present LemmaTag, a featureless neural network architecture that jointly generates part-of-speech tags and lemmas for sentences by using bidirectional RNNs with character-level…