syntactic recordering in statistical machine translation

Reordering has been an important topic in statistical machine translation (SMT) as long as SMT has been around. State-of-the-art SMT systems such as Pharaoh (Koehn, 2004a) still employ a simplistic model of the reordering process to do non-local reordering. This model penalizes any reordering no matter the words. The reordering is only selected if it leads to a translation that looks like a much better sentence than the alternative. Recent developments have, however, seen improvements in translation quality following from syntax-based reordering. One such development is the pre-translation approach that adjusts the source sentence to resemble target language word order prior to translation. This is done based on rules that are either manually created or automatically learned from word aligned parallel corpora. We introduce a novel approach to syntactic reordering. This approach provides better exploitation of the information in the reordering rules and eliminates problematic biases of previous approaches. Although the approach is examined within a pre-translation reordering framework, it easily extends to other frameworks. Our approach significantly outperforms a state-of-the-art phrase-based SMT system and previous approaches to pretranslation reordering, including (Li et al., 2007; Zhang et al., 2007b; Crego & Mari˜ no, 2007). This is consistent both for a very close language pair, English-Danish, and a very distant language pair, English-Arabic. We also propose automatic reordering rule learning based on a rich set of linguistic information. As opposed to most previous approaches that extract a large set of rules, our approach produces a small set of predominantly general rules. These provide a good reflection of the main reordering issues of a given language pair. We examine the influence of several parameters that may have influence on the quality of the rules learned. Finally, we provide a new approach for improving automatic word alignment. This word alignment is used in the above task of automatically learning reordering rules. Our approach learns from hand aligned data how to combine several automatic word alignments to one superior word alignment. The automatic word alignments are created from the same data that has been preprocessed with different tokenization schemes. Thus utilizing the different strengths that different tokenization schemes exhibit in word alignment. We achieve a 38% error reduction for the automatic word alignmen

A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena

Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency. Despite the vast amount of research published to date, the interest of the community in this problem has not decreased, and no single method appears to be strongly dominant across language pairs. Instead, the choice of the optimal approach for a new translation task still seems to be mostly driven by empirical trials. To orientate the reader in this vast and complex research area, we present a comprehensive survey of word reordering viewed as a statistical modeling challenge and as a natural language phenomenon. The survey describes in detail how word reordering is modeled within different string-based and tree-based SMT frameworks and as a stand-alone task, including systematic overviews of the literature in advanced reordering modeling. We then question why some approaches are more successful than others in different language pairs. We argue that, besides measuring the amount of reordering, it is important to understand which kinds of reordering occur in a given language pair. To this end, we conduct a qualitative analysis of word reordering phenomena in a diverse sample of language pairs, based on a large collection of linguistic knowledge. Empirical results in the SMT literature are shown to support the hypothesis that a few linguistic facts can be very useful to anticipate the reordering characteristics of a language pair and to select the SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic

The impact of source-side syntactic reordering on hierarchical phrase-based SMT

Syntactic reordering has been demonstrated to be helpful and effective for handling different word orders between source and target languages in SMT. However, in terms of hierarchial PB-SMT (HPB), does the syntactic reordering still has a significant impact on its performance? This paper introduces a reordering approach which explores the { (DE) grammatical structure in Chinese. We employ the Stanford DE classifier to recognise the DE structures in both training and test sentences of Chinese, and then perform word reordering to make the Chinese sentences better match the word order of English. The annotated and reordered training data and test data are applied to a re-implemented HPB system and the impact of the DE construction is examined. The experiments are conducted on the NIST 2008 evaluation data and experimental results show that the BLEU and METEOR scores are significantly improved by 1.83/8.91 and 1.17/2.73 absolute/ relative points respectively

Description of the Chinese-to-Spanish rule-based machine translation system developed with a hybrid combination of human annotation and statistical techniques

Two of the most popular Machine Translation (MT) paradigms are rule based (RBMT) and corpus based, which include the statistical systems (SMT). When scarce parallel corpus is available, RBMT becomes particularly attractive. This is the case of the Chinese--Spanish language pair. This article presents the first RBMT system for Chinese to Spanish. We describe a hybrid method for constructing this system taking advantage of available resources such as parallel corpora that are used to extract dictionaries and lexical and structural transfer rules. The final system is freely available online and open source. Although performance lags behind standard SMT systems for an in-domain test set, the results show that the RBMT’s coverage is competitive and it outperforms the SMT system in an out-of-domain test set. This RBMT system is available to the general public, it can be further enhanced, and it opens up the possibility of creating future hybrid MT systems.Peer ReviewedPostprint (author's final draft

Exploiting alignment techniques in MATREX: the DCU machine translation system for IWSLT 2008

In this paper, we give a description of the machine translation (MT) system developed at DCU that was used for our third participation in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT 2008). In this participation, we focus on various techniques for word and phrase alignment to improve system quality. Specifically, we try out our word packing and syntax-enhanced word alignment techniques for the Chinese–English task and for the English–Chinese task for the first time. For all translation tasks except Arabic–English, we exploit linguistically motivated bilingual phrase pairs extracted from parallel treebanks. We smooth our translation tables with out-of-domain word translations for the Arabic–English and Chinese–English tasks in order to solve the problem of the high number of out of vocabulary items. We also carried out experiments combining both in-domain and out-of-domain data to improve system performance and, finally, we deploy a majority voting procedure combining a language model based method and a translation-based method for case and punctuation restoration. We participated in all the translation tasks and translated both the single-best ASR hypotheses and the correct recognition results. The translation results confirm that our new word and phrase alignment techniques are often helpful in improving translation quality, and the data combination method we proposed can significantly improve system performance

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project