5,086 research outputs found
Multilingual NMT with a language-independent attention bridge
In this paper, we propose a multilingual encoder-decoder architecture capable
of obtaining multilingual sentence representations by means of incorporating an
intermediate {\em attention bridge} that is shared across all languages. That
is, we train the model with language-specific encoders and decoders that are
connected via self-attention with a shared layer that we call attention bridge.
This layer exploits the semantics from each language for performing translation
and develops into a language-independent meaning representation that can
efficiently be used for transfer learning. We present a new framework for the
efficient development of multilingual NMT using this model and scheduled
training. We have tested the approach in a systematic way with a multi-parallel
data set. We show that the model achieves substantial improvements over strong
bilingual models and that it also works well for zero-shot translation, which
demonstrates its ability of abstraction and transfer learning
Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary
We propose a method to transfer knowledge across neural machine translation
(NMT) models by means of a shared dynamic vocabulary. Our approach allows to
extend an initial model for a given language pair to cover new languages by
adapting its vocabulary as long as new data become available (i.e., introducing
new vocabulary items if they are not included in the initial model). The
parameter transfer mechanism is evaluated in two scenarios: i) to adapt a
trained single language NMT system to work with a new language pair and ii) to
continuously add new language pairs to grow to a multilingual NMT system. In
both the scenarios our goal is to improve the translation performance, while
minimizing the training convergence time. Preliminary experiments spanning five
languages with different training data sizes (i.e., 5k and 50k parallel
sentences) show a significant performance gain ranging from +3.85 up to +13.63
BLEU in different language directions. Moreover, when compared with training an
NMT model from scratch, our transfer-learning approach allows us to reach
higher performance after training up to 4% of the total training steps.Comment: Published at the International Workshop on Spoken Language
Translation (IWSLT), 201
Contextual Parameter Generation for Universal Neural Machine Translation
We propose a simple modification to existing neural machine translation (NMT)
models that enables using a single universal model to translate between
multiple languages while allowing for language specific parameterization, and
that can also be used for domain adaptation. Our approach requires no changes
to the model architecture of a standard NMT system, but instead introduces a
new component, the contextual parameter generator (CPG), that generates the
parameters of the system (e.g., weights in a neural network). This parameter
generator accepts source and target language embeddings as input, and generates
the parameters for the encoder and the decoder, respectively. The rest of the
model remains unchanged and is shared across all languages. We show how this
simple modification enables the system to use monolingual data for training and
also perform zero-shot translation. We further show it is able to surpass
state-of-the-art performance for both the IWSLT-15 and IWSLT-17 datasets and
that the learned language embeddings are able to uncover interesting
relationships between languages.Comment: Published in the proceedings of Empirical Methods in Natural Language
Processing (EMNLP), 201
Neural Machine Translation into Language Varieties
Both research and commercial machine translation have so far neglected the
importance of properly handling the spelling, lexical and grammar divergences
occurring among language varieties. Notable cases are standard national
varieties such as Brazilian and European Portuguese, and Canadian and European
French, which popular online machine translation services are not keeping
distinct. We show that an evident side effect of modeling such varieties as
unique classes is the generation of inconsistent translations. In this work, we
investigate the problem of training neural machine translation from English to
specific pairs of language varieties, assuming both labeled and unlabeled
parallel texts, and low-resource conditions. We report experiments from English
to two pairs of dialects, EuropeanBrazilian Portuguese and European-Canadian
French, and two pairs of standardized varieties, Croatian-Serbian and
Indonesian-Malay. We show significant BLEU score improvements over baseline
systems when translation into similar languages is learned as a multilingual
task with shared representations.Comment: Published at EMNLP 2018: third conference on machine translation (WMT
2018
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