A Gold Standard for English–Swedish Word Alignment

Proceedings of the 18th Nordic Conference of Computational Linguistics NODALIDA 2011. Editors: Bolette Sandford Pedersen, Gunta Nešpore and Inguna Skadiņa. NEALT Proceedings Series, Vol. 11 (2011), 106-113. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/16955

Getting Past the Language Gap: Innovations in Machine Translation

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT

Constrained word alignment models for statistical machine translation

Word alignment is a fundamental and crucial component in Statistical Machine Translation (SMT) systems. Despite the enormous progress made in the past two decades, this task remains an active research topic simply because the quality of word alignment is still far from optimal. Most state-of-the-art word alignment models are grounded on statistical learning theory treating word alignment as a general sequence alignment problem, where many linguistically motivated insights are not incorporated. In this thesis, we propose new word alignment models with linguistically motivated constraints in a bid to improve the quality of word alignment for Phrase-Based SMT systems (PB-SMT). We start the exploration with an investigation into segmentation constraints for word alignment by proposing a novel algorithm, namely word packing, which is motivated by the fact that one concept expressed by one word in one language can frequently surface as a compound or collocation in another language. Our algorithm takes advantage of the interaction between segmentation and alignment, starting with some segmentation for both the source and target language and updating the segmentation with respect to the word alignment results using state-of-the-art word alignment models; thereafter a refined word alignment can be obtained based on the updated segmentation. In this process, the updated segmentation acts as a hard constraint on the word alignment models and reduces the complexity of the alignment models by generating more 1-to-1 correspondences through word packing. Experimental results show that this algorithm can lead to statistically significant improvements over the state-of-the-art word alignment models. Given that word packing imposes "hard" segmentation constraints on the word aligner, which is prone to introducing noise, we propose two new word alignment models using syntactic dependencies as soft constraints. The first model is a syntactically enhanced discriminative word alignment model, where we use a set of feature functions to express the syntactic dependency information encoded in both source and target languages. One the one hand, this model enjoys great flexibility in its capacity to incorporate multiple features; on the other hand, this model is designed to facilitate model tuning for different objective functions. Experimental results show that using syntactic constraints can improve the performance of the discriminative word alignment model, which also leads to better PB-SMT performance compared to using state-of-the-art word alignment models. The second model is a syntactically constrained generative word alignment model, where we add in a syntactic coherence model over the target phrases in the context of HMM word-to-phrase alignment. The advantages of our model are that (i) the addition of the syntactic coherence model preserves the efficient parameter estimation procedures; and (ii) the flexibility of the model can be increased so that it can be tuned according to different objective functions. Experimental results show that tuning this model properly leads to a significant gain in MT performance over the state-of-the-art

Getting Past the Language Gap: Innovations in Machine Translation

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT

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

Semi-supervised training for statistical word alignment

We introduce a semi-supervised approach to training for statistical machine translation that alternates the traditional Expectation Maximization step that is applied on a large training corpus with a discriminative step aimed at increasing word-alignment quality on a small, manually word-aligned sub-corpus. We show that our algorithm leads not only to improved alignments but also to machine translation outputs of higher quality.

Empirical machine translation and its evaluation

Aquesta tesi estudia l'aplicació de les tecnologies del Processament del Llenguatge Natural disponibles actualment al problema de la Traducció Automàtica basada en Mètodes Empírics i la seva Avaluació.D'una banda, tractem el problema de l'avaluació automàtica. Hem analitzat les principals deficiències dels mètodes d'avaluació actuals, les quals es deuen, al nostre parer, als principis de qualitat superficials en els que es basen. En comptes de limitar-nos al nivell lèxic, proposem una nova direcció cap a avaluacions més heterogènies. El nostre enfocament es basa en el disseny d'un ric conjunt de mesures automàtiques destinades a capturar un ampli ventall d'aspectes de qualitat a diferents nivells lingüístics (lèxic, sintàctic i semàntic). Aquestes mesures lingüístiques han estat avaluades sobre diferents escenaris. El resultat més notable ha estat la constatació de que les mètriques basades en un coneixement lingüístic més profund (sintàctic i semàntic) produeixen avaluacions a nivell de sistema més fiables que les mètriques que es limiten a la dimensió lèxica, especialment quan els sistemes avaluats pertanyen a paradigmes de traducció diferents. Tanmateix, a nivell de frase, el comportament d'algunes d'aquestes mètriques lingüístiques empitjora lleugerament en comparació al comportament de les mètriques lèxiques. Aquest fet és principalment atribuïble als errors comesos pels processadors lingüístics. A fi i efecte de millorar l'avaluació a nivell de frase, a més de recòrrer a la similitud lèxica en absència d'anàlisi lingüística, hem estudiat la possibiliat de combinar les puntuacions atorgades per mètriques a diferents nivells lingüístics en una sola mesura de qualitat. S'han presentat dues estratègies no paramètriques de combinació de mètriques, essent el seu principal avantatge no haver d'ajustar la contribució relativa de cadascuna de les mètriques a la puntuació global. A més, el nostre treball mostra com fer servir el conjunt de mètriques heterogènies per tal d'obtenir detallats informes d'anàlisi d'errors automàticament.D'altra banda, hem estudiat el problema de la selecció lèxica en Traducció Automàtica Estadística. Amb aquesta finalitat, hem construit un sistema de Traducció Automàtica Estadística Castellà-Anglès basat en -phrases', i hem iterat en el seu cicle de desenvolupament, analitzant diferents maneres de millorar la seva qualitat mitjançant la incorporació de coneixement lingüístic. En primer lloc, hem extès el sistema a partir de la combinació de models de traducció basats en anàlisi sintàctica superficial, obtenint una millora significativa. En segon lloc, hem aplicat models de traducció discriminatius basats en tècniques d'Aprenentatge Automàtic. Aquests models permeten una millor representació del contexte de traducció en el que les -phrases' ocorren, efectivament conduint a una millor selecció lèxica. No obstant, a partir d'avaluacions automàtiques heterogènies i avaluacions manuals, hem observat que les millores en selecció lèxica no comporten necessàriament una millor estructura sintàctica o semàntica. Així doncs, la incorporació d'aquest tipus de prediccions en el marc estadístic requereix, per tant, un estudi més profund.Com a qüestió complementària, hem estudiat una de les principals crítiques en contra dels sistemes de traducció basats en mètodes empírics, la seva forta dependència del domini, i com els seus efectes negatius poden ésser mitigats combinant adequadament fonts de coneixement externes. En aquest sentit, hem adaptat amb èxit un sistema de traducció estadística Anglès-Castellà entrenat en el domini polític, al domini de definicions de diccionari.Les dues parts d'aquesta tesi estan íntimament relacionades, donat que el desenvolupament d'un sistema real de Traducció Automàtica ens ha permès viure en primer terme l'important paper dels mètodes d'avaluació en el cicle de desenvolupament dels sistemes de Traducció Automàtica.In this thesis we have exploited current Natural Language Processing technology for Empirical Machine Translation and its Evaluation.On the one side, we have studied the problem of automatic MT evaluation. We have analyzed the main deficiencies of current evaluation methods, which arise, in our opinion, from the shallow quality principles upon which they are based. Instead of relying on the lexical dimension alone, we suggest a novel path towards heterogeneous evaluations. Our approach is based on the design of a rich set of automatic metrics devoted to capture a wide variety of translation quality aspects at different linguistic levels (lexical, syntactic and semantic). Linguistic metrics have been evaluated over different scenarios. The most notable finding is that metrics based on deeper linguistic information (syntactic/semantic) are able to produce more reliable system rankings than metrics which limit their scope to the lexical dimension, specially when the systems under evaluation are different in nature. However, at the sentence level, some of these metrics suffer a significant decrease, which is mainly attributable to parsing errors. In order to improve sentence-level evaluation, apart from backing off to lexical similarity in the absence of parsing, we have also studied the possibility of combining the scores conferred by metrics at different linguistic levels into a single measure of quality. Two valid non-parametric strategies for metric combination have been presented. These offer the important advantage of not having to adjust the relative contribution of each metric to the overall score. As a complementary issue, we show how to use the heterogeneous set of metrics to obtain automatic and detailed linguistic error analysis reports.On the other side, we have studied the problem of lexical selection in Statistical Machine Translation. For that purpose, we have constructed a Spanish-to-English baseline phrase-based Statistical Machine Translation system and iterated across its development cycle, analyzing how to ameliorate its performance through the incorporation of linguistic knowledge. First, we have extended the system by combining shallow-syntactic translation models based on linguistic data views. A significant improvement is reported. This system is further enhanced using dedicated discriminative phrase translation models. These models allow for a better representation of the translation context in which phrases occur, effectively yielding an improved lexical choice. However, based on the proposed heterogeneous evaluation methods and manual evaluations conducted, we have found that improvements in lexical selection do not necessarily imply an improved overall syntactic or semantic structure. The incorporation of dedicated predictions into the statistical framework requires, therefore, further study.As a side question, we have studied one of the main criticisms against empirical MT systems, i.e., their strong domain dependence, and how its negative effects may be mitigated by properly combining outer knowledge sources when porting a system into a new domain. We have successfully ported an English-to-Spanish phrase-based Statistical Machine Translation system trained on the political domain to the domain of dictionary definitions.The two parts of this thesis are tightly connected, since the hands-on development of an actual MT system has allowed us to experience in first person the role of the evaluation methodology in the development cycle of MT systems

Semi-supervised training for statistical word alignment

We introduce a semi-supervised approach to training for statistical machine translation that alternates the traditional Expectation Maximization step that is applied on a large training corpus with a discriminative step aimed at increasing word-alignment quality on a small, manually word-aligned sub-corpus. We show that our algorithm leads not only to improved alignments but also to machine translation outputs of higher quality.