111,452 research outputs found
Neural pre-translation for hybrid machine translation
Hybrid machine translation (HMT) takes advantage of different types of machine translation
(MT) systems to improve translation performance. Neural machine translation (NMT) can
produce more fluent translations while phrase-based statistical machine translation (PB-SMT)
can produce adequate results primarily due to the contribution of the translation model. In
this paper, we propose a cascaded hybrid framework to combine NMT and PB-SMT to improve translation quality. Specifically, we first use the trained NMT system to pre-translate
the training data, and then employ the pre-translated training data to build an SMT system and
tune parameters using the pre-translated development set. Finally, the SMT system is utilised
as a post-processing step to re-decode the pre-translated test set and produce the final result.
Experiments conducted on Japanese!English and Chinese!English show that the proposed
cascaded hybrid framework can significantly improve performance by 2.38 BLEU points and
4.22 BLEU points, respectively, compared to the baseline NMT system
Learning to Translate in Real-time with Neural Machine Translation
Translating in real-time, a.k.a. simultaneous translation, outputs
translation words before the input sentence ends, which is a challenging
problem for conventional machine translation methods. We propose a neural
machine translation (NMT) framework for simultaneous translation in which an
agent learns to make decisions on when to translate from the interaction with a
pre-trained NMT environment. To trade off quality and delay, we extensively
explore various targets for delay and design a method for beam-search
applicable in the simultaneous MT setting. Experiments against state-of-the-art
baselines on two language pairs demonstrate the efficacy of the proposed
framework both quantitatively and qualitatively.Comment: 10 pages, camera read
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
Unsupervised Neural Machine Translation with SMT as Posterior Regularization
Without real bilingual corpus available, unsupervised Neural Machine
Translation (NMT) typically requires pseudo parallel data generated with the
back-translation method for the model training. However, due to weak
supervision, the pseudo data inevitably contain noises and errors that will be
accumulated and reinforced in the subsequent training process, leading to bad
translation performance. To address this issue, we introduce phrase based
Statistic Machine Translation (SMT) models which are robust to noisy data, as
posterior regularizations to guide the training of unsupervised NMT models in
the iterative back-translation process. Our method starts from SMT models built
with pre-trained language models and word-level translation tables inferred
from cross-lingual embeddings. Then SMT and NMT models are optimized jointly
and boost each other incrementally in a unified EM framework. In this way, (1)
the negative effect caused by errors in the iterative back-translation process
can be alleviated timely by SMT filtering noises from its phrase tables;
meanwhile, (2) NMT can compensate for the deficiency of fluency inherent in
SMT. Experiments conducted on en-fr and en-de translation tasks show that our
method outperforms the strong baseline and achieves new state-of-the-art
unsupervised machine translation performance.Comment: To be presented at AAAI 2019; 9 pages, 4 figure
Source side pre-ordering using recurrent neural networks for English-Myanmar machine translation
Word reordering has remained one of the challenging problems for machine translation when translating between language pairs with different word orders e.g. English and Myanmar. Without reordering between these languages, a source sentence may be translated directly with similar word order and translation can not be meaningful. Myanmar is a subject-objectverb (SOV) language and an effective reordering is essential for translation. In this paper, we applied a pre-ordering approach using recurrent neural networks to pre-order words of the source Myanmar sentence into target English’s word order. This neural pre-ordering model is automatically derived from parallel word-aligned data with syntactic and lexical features based on dependency parse trees of the source sentences. This can generate arbitrary permutations that may be non-local on the sentence and can be combined into English-Myanmar machine translation. We exploited the model to reorder English sentences into Myanmar-like word order as a preprocessing stage for machine translation, obtaining improvements quality comparable to baseline rule-based pre-ordering approach on asian language treebank (ALT) corpus
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