Monte Carlo techniques for phrase-based translation

Arun, Abhishek; Haddow, Barry; Koehn, Philipp; Lopez, Adam; Dyer, Chris; Blunsom, Phil

doi:10.1007/s10590-010-9080-7

Cited by 4 publications

(3 citation statements)

References 19 publications

(16 reference statements)

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“…Langlais et al (2007) described a greedy search decoder, first introduced in (Germann et al, 2001), able to improve translations produced by a dynamic programming decoder using the same scoring function and translation table. However, the more recent work by Arun et al (2010) using a Gibbs sampler for approximating maximum translation decoding showed the adequacy of the approxima-tions made by state-of-the-art decoders for finding the best translation in their search space. Other works were more directly targeted at automatic post-editing of SMT output, and approached the problem as one of second-pass translation between automatic predictions and correct translations (Simard et al, 2007;Dugast et al, 2007).…”

Section: Related Workmentioning

confidence: 99%

Confidence-based Rewriting of Machine Translation Output

Marie

Max

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Numerous works in Statistical Machine Translation (SMT) have attempted to identify better translation hypotheses obtained by an initial decoding using an improved, but more costly scoring function. In this work, we introduce an approach that takes the hypotheses produced by a state-ofthe-art, reranked phrase-based SMT system, and explores new parts of the search space by applying rewriting rules selected on the basis of posterior phraselevel confidence. In the medical domain, we obtain a 1.9 BLEU improvement over a reranked baseline exploiting the same scoring function, corresponding to a 5.4 BLEU improvement over the original Moses baseline. We show that if an indication of which phrases require rewriting is provided, our automatic rewriting procedure yields an additional improvement of 1.5 BLEU. Various analyses, including a manual error analysis, further illustrate the good performance and potential for improvement of our approach in spite of its simplicity.

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Section: Related Workmentioning

confidence: 99%

Confidence-based Rewriting of Machine Translation Output

Marie

Max

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…[16] use a Monte Carlo technique for decoding phrase based translation lattices. Bi-directional learning uses a Perceptron to iteratively label each position of a word sequence, updating parameters when a single incorrect label is applied [17].…”

Section: Discriminative Hill Climbing Trainingmentioning

confidence: 99%

Efficient discriminative training of long-span language models

Rastrow

Dredze

Khudanpur

2011

2011 IEEE Workshop on Automatic Speech Recognition &Amp; Understanding

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Abstract-Long-span language models, such as those involving syntactic dependencies, produce more coherent text than their n-gram counterparts. However, evaluating the large number of sentence-hypotheses in a packed representation such as an ASR lattice is intractable under such long-span models both during decoding and discriminative training. The accepted compromise is to rescore only the N -best hypotheses in the lattice using the long-span LM. We present discriminative hill climbing, an efficient and effective discriminative training procedure for longspan LMs based on a hill climbing rescoring algorithm [1]. We empirically demonstrate significant computational savings as well as error-rate reduction over N -best training methods in a state of the art ASR system for Broadcast News transcription.

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“…Alternatively, several works have extended the model of Koehn et al (2003) by, instead of performing workarounds to its limitations, replacing the beam search algorithm altogether. For instance, Arun et al (2010) use a Gibbs sampler to draw samples from the posterior distribution. The sampler consists of three operators that, applied probabilistically, explore the distribution.…”

Section: Document-level Statistical Machine Translationmentioning

confidence: 99%

Document-level machine translation : ensuring translational consistency of non-local phenomena

Martínez Garcia

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In this thesis, we study the automatic translation of documents by taking into account cross-sentence phenomena. This document-level information is typically ignored by most of the standard state-of-the-art Machine Translation (MT) systems, which focus on translating texts processing each of their sentences in isolation. Translating each sentence without looking at its surrounding context can lead to certain types of translation errors, such as inconsistent translations for the same word or for elements in a coreference chain. We introduce methods to attend to document-level phenomena in order to avoid those errors, and thus, reach translations that properly convey the original meaning. Our research starts by identifying the translation errors related to such document-level phenomena that commonly appear in the output of state-of-the-art Statistical Machine Translation (SMT) systems. For two of those errors, namely inconsistent word translations as well as gender and number disagreements among words, we design simple and yet effective post-processing techniques to tackle and correct them. Since these techniques are applied a posteriori, they can access the whole source and target documents, and hence, they are able to perform a global analysis and improve the coherence and consistency of the translation. Nevertheless, since following such a two-pass decoding strategy is not optimal in terms of efficiency, we also focus on introducing the context-awareness during the decoding process itself. To this end, we enhance a document-oriented SMT system with distributional semantic information in the form of bilingual and monolingual word embeddings. In particular, these embeddings are used as Semantic Space Language Models (SSLMs) and as a novel feature function. The goal of the former is to promote word translations that are semantically close to their preceding context, whereas the latter promotes the lexical choice that is closest to its surrounding context, for those words that have varying translations throughout the document. In both cases, the context extends beyond sentence boundaries. Recently, the MT community has transitioned to the neural paradigm. The finalstep of our research proposes an extension of the decoding process for a Neural Machine Translation (NMT) framework, independent of the model architecture, by shallow fusing the information from a neural translation model and the context semantics enclosed in the previously studied SSLMs. The aim of this modification is to introduce the benefits of context information also into the decoding process of NMT systems, as well as to obtain an additional validation for the techniques we explored. The automatic evaluation of our approaches does not reflect significant variations. This is expected since most automatic metrics are neither context-nor semantic-aware and because the phenomena we tackle are rare, leading to few modifications with respect to the baseline translations. On the other hand, manual evaluations demonstrate the positive impact of our approaches since human evaluators tend to prefer the translations produced by our document-aware systems. Therefore, the changes introduced by our enhanced systems are important since they are related to how humans perceive translation quality for long texts. En esta tesis se estudia la traducción automática de documentos teniendo en cuenta fenómenos que ocurren entre oraciones. Típicamente, esta información a nivel de documento se ignora por la mayoría de los sistemas de Traducción Automática (MT), que se centran en traducir los textos procesando cada una de las frases que los componen de manera aislada. Traducir cada frase sin mirar al contexto que la rodea puede llevar a generar cierto tipo de errores de traducción, como pueden ser traducciones inconsistentes para la misma palabra o para elementos que aparecen en la misma cadena de correferencia. En este trabajo se presentan métodos para prestar atención a fenómenos a nivel de documento con el objetivo de evitar este tipo de errores y así llegar a generar traducciones que transmitan correctamente el significado original del texto. Nuestra investigación empieza por identificar los errores de traducción relacionados con los fenómenos a nivel de documento que aparecen de manera común en la salida de los sistemas Estadísticos del Traducción Automática (SMT). Para dos de estos errores, la traducción inconsistente de palabras, así como los desacuerdos en género y número entre palabras, diseñamos técnicas simples pero efectivas como post-procesos para tratarlos y corregirlos. Como estas técnicas se aplican a posteriori, pueden acceder a los documentos enteros tanto del origen como la traducción generada, y así son capaces de hacer un análisis global y mejorar la coherencia y la consistencia de la traducción. Sin embargo, como seguir una estrategia de traducción en dos pasos no es óptima en términos de eficiencia, también nos centramos en introducir la conciencia del contexto durante el propio proceso de generación de la traducción. Para esto, extendemos un sistema SMT orientado a documentos incluyendo información semántica distribucional en forma de word embeddings bilingües y monolingües. En particular, estos embeddings se usan como un Modelo de Lenguaje de Espacio Semántico (SSLM) y como una nueva función característica del sistema. La meta del primero es promover traducciones de palabras que sean semánticamente cercanas a su contexto precedente, mientras que la segunda quiere promover la selección léxica que es más cercana a su contexto para aquellas palabras que tienen diferentes traducciones a lo largo de un documento. En ambos casos, el contexto que se tiene en cuenta va más allá de los límites de una frase u oración. Recientemente, la comunidad MT ha hecho una transición hacia el paradigma neuronal. El paso final de nuestra investigación propone una extensión del proceso de decodificación de un sistema de Traducción Automática Neuronal (NMT), independiente de la arquitectura del modelo de traducción, aplicando la técnica de Shallow Fusion para combinar la información del modelo de traducción neuronal y la información semántica del contexto encerrada en los modelos SSLM estudiados previamente. La motivación de esta modificación está en introducir los beneficios de la información del contexto también en el proceso de decodificación de los sistemas NMT, así como también obtener una validación adicional para las técnicas que se han ido explorando a lo largo de esta tesis. La evaluación automática de nuestras propuestas no refleja variaciones significativas. Esto es un comportamiento esperado ya que la mayoría de las métricas automáticas no se diseñan para ser sensibles al contexto o a la semántica, y además los fenómenos que tratamos son escasos, llevando a pocas modificaciones con respecto a las traducciones de partida. Por otro lado, las evaluaciones manuales demuestran el impacto positivo de nuestras propuestas ya que los evaluadores humanos tienen a preferir las traducciones generadas por nuestros sistemas a nivel de documento. Entonces, los cambios introducidos por nuestros sistemas extendidos son importantes porque están relacionados con la forma en que los humanos perciben la calidad de la traducción de textos largos.

show abstract

Monte Carlo techniques for phrase-based translation

Cited by 4 publications

References 19 publications

Confidence-based Rewriting of Machine Translation Output

Confidence-based Rewriting of Machine Translation Output

Efficient discriminative training of long-span language models

Document-level machine translation : ensuring translational consistency of non-local phenomena

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