163 citations · 201 across the 11 of their papers we have counts for
12 papers · 1 filter
High-Resource Methodological Bias in Low-Resource Investigations
Maartje ter Hoeve, David Grangier, Natalie Schluter
The central bottleneck for low-resource NLP is typically regarded to be the quantity of accessible data, overlooking the contribution of data quality. This is particularly seen in…
On the Complementarity of Data Selection and Fine Tuning for Domain Adaptation
Dan Iter, David Grangier
Domain adaptation of neural networks commonly relies on three training phases: pretraining, selected data training and then fine tuning. Data selection improves target domain gener…
Human-Paraphrased References Improve Neural Machine Translation
Markus Freitag, George Foster, David Grangier +1
Automatic evaluation comparing candidate translations to human-generated paraphrases of reference translations has recently been proposed by Freitag et al. When used in place of or…
Toward Better Storylines with Sentence-Level Language Models
Daphne Ippolito, David Grangier, Douglas Eck +1
We propose a sentence-level language model which selects the next sentence in a story from a finite set of fluent alternatives. Since it does not need to model fluency, the sentenc…
BLEU might be Guilty but References are not Innocent
Markus Freitag, David Grangier, Isaac Caswell
The quality of automatic metrics for machine translation has been increasingly called into question, especially for high-quality systems. This paper demonstrates that, while choice…
Translationese as a Language in "Multilingual" NMT
Parker Riley, Isaac Caswell, Markus Freitag +1
Machine translation has an undesirable propensity to produce "translationese" artifacts, which can lead to higher BLEU scores while being liked less by human raters. Motivated by t…