36,727 research outputs found
Continuous spaces in statistical machine Translation
[EN] Classically, statistical machine translation relied on representations of words in a
discrete space. Words and phrases were atomically represented as indices in a
vector. In the last years, techniques for representing words and phrases in a
continuous space have arisen. In this scenario, a word is represented in the
continuous space as a real-valued, dense and low-dimensional vector. Statistical
models can profit from this richer representation, since it is able to naturally take
into account concepts such as semantic or syntactic relationships between words
and phrases. This approach is encouraging, but it also entails new challenges.
In this work, a language model which relies on continuous representations of
words is developed. Such model makes use of a bidirectional recurrent neural
network, which is able to take into account both the past and the future context
of words. Since the model is costly to train, the training dataset is reduced by
using bilingual sentence selection techniques. Two selection methods are used
and compared. The language model is then used to rerank translation
hypotheses. Results show improvements on the translation quality.
Moreover, a new approach for machine translation has been recently proposed:
The so-called neural machine translation. It consists in the sole use of a large
neural network for carrying out the translation process. In this work, such novel
model is compared to the existing phrase-based approaches of statistical
machine translation.
Finally, the neural translation models are combined with diverse machine
translation systems, in order to provide a consensus translation, which aim to
improve the translation given by each single system.[ES] Los sistemas clásicos de traducción automática estadística están basados en
representaciones de palabras en un espacio discreto. Palabras y segmentos se
representan como índices en un vector. Durante los últimos años han surgido
técnicas para realizar la representación de palabras y segmentos en un espacio
continuo. En este escenario, una palabra se representa en el espacio continuo
como un vector de valores reales, denso y de baja dimensión. Los modelos
estadísticos pueden aprovecharse de esta representación más rica, puesto que
incluye de forma natural conceptos semánticos o relaciones sintácticas entre
palabras y segmentos. Esta aproximación es prometedora, pero también conlleva
nuevos retos.
En este trabajo se desarrolla un modelo de lenguaje basado en representaciones
continuas de palabras. Dicho modelo emplea una red neuronal recurrente
bidireccional, la cual es capaz de considerar tanto el contexto pasado como el
contexto futuro de las palabras. Debido a que este modelo es costoso de
entrenar, se emplea un conjunto de entrenamiento reducido mediante técnicas
de selección de frases bilingües. Se emplean y comparan dos métodos de
selección. Una vez entrenado, el modelo se emplea para reordenar hipótesis de
traducción. Los resultados muestran mejoras en la calidad de la traducción.
Por otro lado, recientemente se propuso una nueva aproximación a la traducción
automática: la llamada traducción automática neuronal. Consiste en el uso
exclusivo de una gran red neuronal para llevar a cabo el proceso de traducción.
En este trabajo, este nuevo modelo se compara al paradigma actual de
traducción basada en segmentos.
Finalmente, los modelos de traducción neuronales son combinados con otros
sistemas de traducción automática, para ofrecer una traducción consensuada,
que busca mejorar las traducciones individuales que cada sistema ofrecePeris Abril, Á. (2015). Continuous spaces in statistical machine Translation. http://hdl.handle.net/10251/68448Archivo delegad
Learning Semantic Representations for the Phrase Translation Model
This paper presents a novel semantic-based phrase translation model. A pair
of source and target phrases are projected into continuous-valued vector
representations in a low-dimensional latent semantic space, where their
translation score is computed by the distance between the pair in this new
space. The projection is performed by a multi-layer neural network whose
weights are learned on parallel training data. The learning is aimed to
directly optimize the quality of end-to-end machine translation results.
Experimental evaluation has been performed on two Europarl translation tasks,
English-French and German-English. The results show that the new semantic-based
phrase translation model significantly improves the performance of a
state-of-the-art phrase-based statistical machine translation sys-tem, leading
to a gain of 0.7-1.0 BLEU points
Domain adaptation strategies in statistical machine translation: a brief overview
© Cambridge University Press, 2015.Statistical machine translation (SMT) is gaining interest given that it can easily be adapted to any pair of languages. One of the main challenges in SMT is domain adaptation because the performance in translation drops when testing conditions deviate from training conditions. Many research works are arising to face this challenge. Research is focused on trying to exploit all kinds of material, if available. This paper provides an overview of research, which copes with the domain adaptation challenge in SMT.Peer ReviewedPostprint (author's final draft
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
In this paper, we propose a novel neural network model called RNN
Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN
encodes a sequence of symbols into a fixed-length vector representation, and
the other decodes the representation into another sequence of symbols. The
encoder and decoder of the proposed model are jointly trained to maximize the
conditional probability of a target sequence given a source sequence. The
performance of a statistical machine translation system is empirically found to
improve by using the conditional probabilities of phrase pairs computed by the
RNN Encoder-Decoder as an additional feature in the existing log-linear model.
Qualitatively, we show that the proposed model learns a semantically and
syntactically meaningful representation of linguistic phrases.Comment: EMNLP 201
BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings
In this paper, we propose a bidimensional attention based recursive
autoencoder (BattRAE) to integrate clues and sourcetarget interactions at
multiple levels of granularity into bilingual phrase representations. We employ
recursive autoencoders to generate tree structures of phrases with embeddings
at different levels of granularity (e.g., words, sub-phrases and phrases). Over
these embeddings on the source and target side, we introduce a bidimensional
attention network to learn their interactions encoded in a bidimensional
attention matrix, from which we extract two soft attention weight distributions
simultaneously. These weight distributions enable BattRAE to generate
compositive phrase representations via convolution. Based on the learned phrase
representations, we further use a bilinear neural model, trained via a
max-margin method, to measure bilingual semantic similarity. To evaluate the
effectiveness of BattRAE, we incorporate this semantic similarity as an
additional feature into a state-of-the-art SMT system. Extensive experiments on
NIST Chinese-English test sets show that our model achieves a substantial
improvement of up to 1.63 BLEU points on average over the baseline.Comment: 7 pages, accepted by AAAI 201
Does Multimodality Help Human and Machine for Translation and Image Captioning?
This paper presents the systems developed by LIUM and CVC for the WMT16
Multimodal Machine Translation challenge. We explored various comparative
methods, namely phrase-based systems and attentional recurrent neural networks
models trained using monomodal or multimodal data. We also performed a human
evaluation in order to estimate the usefulness of multimodal data for human
machine translation and image description generation. Our systems obtained the
best results for both tasks according to the automatic evaluation metrics BLEU
and METEOR.Comment: 7 pages, 2 figures, v4: Small clarification in section 4 title and
conten
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