175 research outputs found
Unfolding and Shrinking Neural Machine Translation Ensembles
Ensembling is a well-known technique in neural machine translation (NMT) to
improve system performance. Instead of a single neural net, multiple neural
nets with the same topology are trained separately, and the decoder generates
predictions by averaging over the individual models. Ensembling often improves
the quality of the generated translations drastically. However, it is not
suitable for production systems because it is cumbersome and slow. This work
aims to reduce the runtime to be on par with a single system without
compromising the translation quality. First, we show that the ensemble can be
unfolded into a single large neural network which imitates the output of the
ensemble system. We show that unfolding can already improve the runtime in
practice since more work can be done on the GPU. We proceed by describing a set
of techniques to shrink the unfolded network by reducing the dimensionality of
layers. On Japanese-English we report that the resulting network has the size
and decoding speed of a single NMT network but performs on the level of a
3-ensemble system.Comment: Accepted at EMNLP 201
Break it Down for Me: A Study in Automated Lyric Annotation
Comprehending lyrics, as found in songs and poems, can pose a challenge to
human and machine readers alike. This motivates the need for systems that can
understand the ambiguity and jargon found in such creative texts, and provide
commentary to aid readers in reaching the correct interpretation. We introduce
the task of automated lyric annotation (ALA). Like text simplification, a goal
of ALA is to rephrase the original text in a more easily understandable manner.
However, in ALA the system must often include additional information to clarify
niche terminology and abstract concepts. To stimulate research on this task, we
release a large collection of crowdsourced annotations for song lyrics. We
analyze the performance of translation and retrieval models on this task,
measuring performance with both automated and human evaluation. We find that
each model captures a unique type of information important to the task.Comment: To appear in Proceedings of EMNLP 201
The Edit Distance Transducer in Action: The University of Cambridge English-German System at WMT16
This paper presents the University of Cambridge submission to WMT16.
Motivated by the complementary nature of syntactical machine translation and
neural machine translation (NMT), we exploit the synergies of Hiero and NMT in
different combination schemes. Starting out with a simple neural lattice
rescoring approach, we show that the Hiero lattices are often too narrow for
NMT ensembles. Therefore, instead of a hard restriction of the NMT search space
to the lattice, we propose to loosely couple NMT and Hiero by composition with
a modified version of the edit distance transducer. The loose combination
outperforms lattice rescoring, especially when using multiple NMT systems in an
ensemble
UCAM Biomedical Translation at WMT19: Transfer Learning Multi-domain Ensembles
The 2019 WMT Biomedical translation task involved translating Medline
abstracts. We approached this using transfer learning to obtain a series of
strong neural models on distinct domains, and combining them into multi-domain
ensembles. We further experiment with an adaptive language-model ensemble
weighting scheme. Our submission achieved the best submitted results on both
directions of English-Spanish
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