426 research outputs found
Meta-Learning for Low-Resource Neural Machine Translation
In this paper, we propose to extend the recently introduced model-agnostic
meta-learning algorithm (MAML) for low-resource neural machine translation
(NMT). We frame low-resource translation as a meta-learning problem, and we
learn to adapt to low-resource languages based on multilingual high-resource
language tasks. We use the universal lexical
representation~\citep{gu2018universal} to overcome the input-output mismatch
across different languages. We evaluate the proposed meta-learning strategy
using eighteen European languages (Bg, Cs, Da, De, El, Es, Et, Fr, Hu, It, Lt,
Nl, Pl, Pt, Sk, Sl, Sv and Ru) as source tasks and five diverse languages (Ro,
Lv, Fi, Tr and Ko) as target tasks. We show that the proposed approach
significantly outperforms the multilingual, transfer learning based
approach~\citep{zoph2016transfer} and enables us to train a competitive NMT
system with only a fraction of training examples. For instance, the proposed
approach can achieve as high as 22.04 BLEU on Romanian-English WMT'16 by seeing
only 16,000 translated words (~600 parallel sentences).Comment: Accepted as a full paper at EMNLP 201
Findings of the 2019 Conference on Machine Translation (WMT19)
This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019.
Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation
Improving both domain robustness and domain adaptability in machine translation
We address two problems of domain adaptation in neural machine translation.
First, we want to reach domain robustness, i.e., good quality of both domains
from the training data, and domains unseen in the training data. Second, we
want our systems to be adaptive, i.e., making it possible to finetune systems
with just hundreds of in-domain parallel sentences. In this paper, we introduce
a novel combination of two previous approaches, word adaptive modelling, which
addresses domain robustness, and meta-learning, which addresses domain
adaptability, and we present empirical results showing that our new combination
improves both of these properties
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