3 research outputs found
Modeling Latent Sentence Structure in Neural Machine Translation
Recently it was shown that linguistic structure predicted by a supervised
parser can be beneficial for neural machine translation (NMT). In this work we
investigate a more challenging setup: we incorporate sentence structure as a
latent variable in a standard NMT encoder-decoder and induce it in such a way
as to benefit the translation task. We consider German-English and
Japanese-English translation benchmarks and observe that when using RNN
encoders the model makes no or very limited use of the structure induction
apparatus. In contrast, CNN and word-embedding-based encoders rely on latent
graphs and force them to encode useful, potentially long-distance,
dependencies.Comment: Accepted as an extended abstract to ACL NMT workshop 201
Findings of the Second Workshop on Neural Machine Translation and Generation
This document describes the findings of the Second Workshop on Neural Machine
Translation and Generation, held in concert with the annual conference of the
Association for Computational Linguistics (ACL 2018). First, we summarize the
research trends of papers presented in the proceedings, and note that there is
particular interest in linguistic structure, domain adaptation, data
augmentation, handling inadequate resources, and analysis of models. Second, we
describe the results of the workshop's shared task on efficient neural machine
translation, where participants were tasked with creating MT systems that are
both accurate and efficient.Comment: WNMT 201
Neural Machine Translation: A Review and Survey
The field of machine translation (MT), the automatic translation of written
text from one natural language into another, has experienced a major paradigm
shift in recent years. Statistical MT, which mainly relies on various
count-based models and which used to dominate MT research for decades, has
largely been superseded by neural machine translation (NMT), which tackles
translation with a single neural network. In this work we will trace back the
origins of modern NMT architectures to word and sentence embeddings and earlier
examples of the encoder-decoder network family. We will conclude with a survey
of recent trends in the field.Comment: Extended version of "Neural Machine Translation: A Review" accepted
by the Journal of Artificial Intelligence Research (JAIR