4,527 research outputs found
Joint Training for Neural Machine Translation Models with Monolingual Data
Monolingual data have been demonstrated to be helpful in improving
translation quality of both statistical machine translation (SMT) systems and
neural machine translation (NMT) systems, especially in resource-poor or domain
adaptation tasks where parallel data are not rich enough. In this paper, we
propose a novel approach to better leveraging monolingual data for neural
machine translation by jointly learning source-to-target and target-to-source
NMT models for a language pair with a joint EM optimization method. The
training process starts with two initial NMT models pre-trained on parallel
data for each direction, and these two models are iteratively updated by
incrementally decreasing translation losses on training data. In each iteration
step, both NMT models are first used to translate monolingual data from one
language to the other, forming pseudo-training data of the other NMT model.
Then two new NMT models are learnt from parallel data together with the pseudo
training data. Both NMT models are expected to be improved and better
pseudo-training data can be generated in next step. Experiment results on
Chinese-English and English-German translation tasks show that our approach can
simultaneously improve translation quality of source-to-target and
target-to-source models, significantly outperforming strong baseline systems
which are enhanced with monolingual data for model training including
back-translation.Comment: Accepted by AAAI 201
Improving Neural Machine Translation Models with Monolingual Data
Neural Machine Translation (NMT) has obtained state-of-the art performance
for several language pairs, while only using parallel data for training.
Target-side monolingual data plays an important role in boosting fluency for
phrase-based statistical machine translation, and we investigate the use of
monolingual data for NMT. In contrast to previous work, which combines NMT
models with separately trained language models, we note that encoder-decoder
NMT architectures already have the capacity to learn the same information as a
language model, and we explore strategies to train with monolingual data
without changing the neural network architecture. By pairing monolingual
training data with an automatic back-translation, we can treat it as additional
parallel training data, and we obtain substantial improvements on the WMT 15
task EnglishGerman (+2.8-3.7 BLEU), and for the low-resourced IWSLT 14 task
Turkish->English (+2.1-3.4 BLEU), obtaining new state-of-the-art results. We
also show that fine-tuning on in-domain monolingual and parallel data gives
substantial improvements for the IWSLT 15 task English->German.Comment: accepted to ACL 2016; new section on effect of back-translation
qualit
Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation
There are several approaches for improving neural machine translation for low-resource languages: monolingual data can be exploited via pretraining or data augmentation; parallel corpora on related language pairs can be used via parameter sharing or transfer learning in multilingual models; subword segmentation and regularization techniques can be applied to ensure high coverage of the vocabulary. We review these approaches in the context of an asymmetric-resource one-to-many translation task, in which the pair of target languages are related, with one being a very low-resource and the other a higher-resource language. We test various methods on three artificially restricted translation tasks—English to Estonian (low-resource) and Finnish (high-resource), English to Slovak and Czech, English to Danish and Swedish—and one real-world task, Norwegian to North Sámi and Finnish. The experiments show positive effects especially for scheduled multi-task learning, denoising autoencoder, and subword sampling.There are several approaches for improving neural machine translation for low-resource languages: monolingual data can be exploited via pretraining or data augmentation; parallel corpora on related language pairs can be used via parameter sharing or transfer learning in multilingual models; subword segmentation and regularization techniques can be applied to ensure high coverage of the vocabulary. We review these approaches in the context of an asymmetric-resource one-to-many translation task, in which the pair of target languages are related, with one being a very low-resource and the other a higher-resource language. We test various methods on three artificially restricted translation tasks-English to Estonian (low-resource) and Finnish (high-resource), English to Slovak and Czech, English to Danish and Swedish-and one real-world task, Norwegian to North Sami and Finnish. The experiments show positive effects especially for scheduled multi-task learning, denoising autoencoder, and subword sampling.Peer reviewe
Semi-Supervised Learning for Neural Machine Translation
While end-to-end neural machine translation (NMT) has made remarkable
progress recently, NMT systems only rely on parallel corpora for parameter
estimation. Since parallel corpora are usually limited in quantity, quality,
and coverage, especially for low-resource languages, it is appealing to exploit
monolingual corpora to improve NMT. We propose a semi-supervised approach for
training NMT models on the concatenation of labeled (parallel corpora) and
unlabeled (monolingual corpora) data. The central idea is to reconstruct the
monolingual corpora using an autoencoder, in which the source-to-target and
target-to-source translation models serve as the encoder and decoder,
respectively. Our approach can not only exploit the monolingual corpora of the
target language, but also of the source language. Experiments on the
Chinese-English dataset show that our approach achieves significant
improvements over state-of-the-art SMT and NMT systems.Comment: Corrected a typ
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