558 research outputs found
Word Representation Models for Morphologically Rich Languages in Neural Machine Translation
Dealing with the complex word forms in morphologically rich languages is an
open problem in language processing, and is particularly important in
translation. In contrast to most modern neural systems of translation, which
discard the identity for rare words, in this paper we propose several
architectures for learning word representations from character and morpheme
level word decompositions. We incorporate these representations in a novel
machine translation model which jointly learns word alignments and translations
via a hard attention mechanism. Evaluating on translating from several
morphologically rich languages into English, we show consistent improvements
over strong baseline methods, of between 1 and 1.5 BLEU points
Natural Language Processing with Small Feed-Forward Networks
We show that small and shallow feed-forward neural networks can achieve near
state-of-the-art results on a range of unstructured and structured language
processing tasks while being considerably cheaper in memory and computational
requirements than deep recurrent models. Motivated by resource-constrained
environments like mobile phones, we showcase simple techniques for obtaining
such small neural network models, and investigate different tradeoffs when
deciding how to allocate a small memory budget.Comment: EMNLP 2017 short pape
From feature to paradigm: deep learning in machine translation
In the last years, deep learning algorithms have highly revolutionized several areas including speech, image and natural language processing. The specific field of Machine Translation (MT) has not remained invariant. Integration of deep learning in MT varies from re-modeling existing features into standard statistical systems to the development of a new architecture. Among the different neural networks, research works use feed- forward neural networks, recurrent neural networks and the encoder-decoder schema. These architectures are able to tackle challenges as having low-resources or morphology variations. This manuscript focuses on describing how these neural networks have been integrated to enhance different aspects and models from statistical MT, including language modeling, word alignment, translation, reordering, and rescoring. Then, we report the new neural MT approach together with a description of the foundational related works and recent approaches on using subword, characters and training with multilingual languages, among others. Finally, we include an analysis of the corresponding challenges and future work in using deep learning in MTPostprint (author's final draft
Pre-reordering for neural machine translation: helpful or harmful?
Pre-reordering, a preprocessing to make the source-side word orders close to those of the
target side, has been proven very helpful for statistical machine translation (SMT) in improving
translation quality. However, is it the case in neural machine translation (NMT)? In this paper,
we firstly investigate the impact of pre-reordered source-side data on NMT, and then propose to
incorporate features for the pre-reordering model in SMT as input factors into NMT (factored
NMT). The features, namely parts-of-speech (POS), word class and reordered index, are encoded as feature vectors and concatenated to the word embeddings to provide extra knowledge
for NMT. Pre-reordering experiments conducted on Japanese↔English and Chinese↔English
show that pre-reordering the source-side data for NMT is redundant and NMT models trained
on pre-reordered data deteriorate translation performance. However, factored NMT using
SMT-based pre-reordering features on Japanese→English and Chinese→English is beneficial
and can further improve by 4.48 and 5.89 relative BLEU points, respectively, compared to the
baseline NMT system
Statistical Machine Translation Features with Multitask Tensor Networks
We present a three-pronged approach to improving Statistical Machine
Translation (SMT), building on recent success in the application of neural
networks to SMT. First, we propose new features based on neural networks to
model various non-local translation phenomena. Second, we augment the
architecture of the neural network with tensor layers that capture important
higher-order interaction among the network units. Third, we apply multitask
learning to estimate the neural network parameters jointly. Each of our
proposed methods results in significant improvements that are complementary.
The overall improvement is +2.7 and +1.8 BLEU points for Arabic-English and
Chinese-English translation over a state-of-the-art system that already
includes neural network features.Comment: 11 pages (9 content + 2 references), 2 figures, accepted to ACL 2015
as a long pape
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