727 research outputs found
Edinburgh Neural Machine Translation Systems for WMT 16
We participated in the WMT 2016 shared news translation task by building
neural translation systems for four language pairs, each trained in both
directions: EnglishCzech, EnglishGerman, EnglishRomanian and
EnglishRussian. Our systems are based on an attentional encoder-decoder,
using BPE subword segmentation for open-vocabulary translation with a fixed
vocabulary. We experimented with using automatic back-translations of the
monolingual News corpus as additional training data, pervasive dropout, and
target-bidirectional models. All reported methods give substantial
improvements, and we see improvements of 4.3--11.2 BLEU over our baseline
systems. In the human evaluation, our systems were the (tied) best constrained
system for 7 out of 8 translation directions in which we participated.Comment: WMT16 shared task system description - final version with human
evaluation result
A Shared Task on Bandit Learning for Machine Translation
We introduce and describe the results of a novel shared task on bandit
learning for machine translation. The task was organized jointly by Amazon and
Heidelberg University for the first time at the Second Conference on Machine
Translation (WMT 2017). The goal of the task is to encourage research on
learning machine translation from weak user feedback instead of human
references or post-edits. On each of a sequence of rounds, a machine
translation system is required to propose a translation for an input, and
receives a real-valued estimate of the quality of the proposed translation for
learning. This paper describes the shared task's learning and evaluation setup,
using services hosted on Amazon Web Services (AWS), the data and evaluation
metrics, and the results of various machine translation architectures and
learning protocols.Comment: Conference on Machine Translation (WMT) 201
Stronger Baselines for Trustable Results in Neural Machine Translation
Interest in neural machine translation has grown rapidly as its effectiveness
has been demonstrated across language and data scenarios. New research
regularly introduces architectural and algorithmic improvements that lead to
significant gains over "vanilla" NMT implementations. However, these new
techniques are rarely evaluated in the context of previously published
techniques, specifically those that are widely used in state-of-theart
production and shared-task systems. As a result, it is often difficult to
determine whether improvements from research will carry over to systems
deployed for real-world use. In this work, we recommend three specific methods
that are relatively easy to implement and result in much stronger experimental
systems. Beyond reporting significantly higher BLEU scores, we conduct an
in-depth analysis of where improvements originate and what inherent weaknesses
of basic NMT models are being addressed. We then compare the relative gains
afforded by several other techniques proposed in the literature when starting
with vanilla systems versus our stronger baselines, showing that experimental
conclusions may change depending on the baseline chosen. This indicates that
choosing a strong baseline is crucial for reporting reliable experimental
results.Comment: To appear at the Workshop on Neural Machine Translation (WNMT
Discourse Structure in Machine Translation Evaluation
In this article, we explore the potential of using sentence-level discourse
structure for machine translation evaluation. We first design discourse-aware
similarity measures, which use all-subtree kernels to compare discourse parse
trees in accordance with the Rhetorical Structure Theory (RST). Then, we show
that a simple linear combination with these measures can help improve various
existing machine translation evaluation metrics regarding correlation with
human judgments both at the segment- and at the system-level. This suggests
that discourse information is complementary to the information used by many of
the existing evaluation metrics, and thus it could be taken into account when
developing richer evaluation metrics, such as the WMT-14 winning combined
metric DiscoTKparty. We also provide a detailed analysis of the relevance of
various discourse elements and relations from the RST parse trees for machine
translation evaluation. In particular we show that: (i) all aspects of the RST
tree are relevant, (ii) nuclearity is more useful than relation type, and (iii)
the similarity of the translation RST tree to the reference tree is positively
correlated with translation quality.Comment: machine translation, machine translation evaluation, discourse
analysis. Computational Linguistics, 201
A Convolutional Encoder Model for Neural Machine Translation
The prevalent approach to neural machine translation relies on bi-directional
LSTMs to encode the source sentence. In this paper we present a faster and
simpler architecture based on a succession of convolutional layers. This allows
to encode the entire source sentence simultaneously compared to recurrent
networks for which computation is constrained by temporal dependencies. On
WMT'16 English-Romanian translation we achieve competitive accuracy to the
state-of-the-art and we outperform several recently published results on the
WMT'15 English-German task. Our models obtain almost the same accuracy as a
very deep LSTM setup on WMT'14 English-French translation. Our convolutional
encoder speeds up CPU decoding by more than two times at the same or higher
accuracy as a strong bi-directional LSTM baseline.Comment: 13 page
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