Mitigating the problems of SMT using EBMT

Statistical Machine Translation (SMT) typically has difficulties with less-resourced languages even with homogeneous data. In this thesis we address the application of Example-Based Machine Translation (EBMT) methods to overcome some of these difficulties. We adopt three alternative approaches to tackle these problems focusing on two poorly-resourced translation tasks (English–Bangla and English–Turkish). First, we adopt a runtime approach to EBMT using proportional analogy. In addition to the translation task, we have tested the EBMT system using proportional analogy for named entity transliteration. In the second attempt, we use a compiled approach to EBMT. Finally, we present a novel way of integrating Translation Memory (TM) into an EBMT system. We discuss the development of these three different EBMT systems and the experiments we have performed. In addition, we present an approach to augment the output quality by strategically combining EBMT systems and SMT systems. The hybrid system shows significant improvement for different language pairs. Runtime EBMT systems in general have significant time complexity issues especially for large example-base. We explore two methods to address this issue in our system by making the system scalable at runtime for a large example-base (English–French). First, we use a heuristic-based approach. Secondly we use an IR-based indexing technique to speed up the time-consuming matching procedure of the EBMT system. The index-based matching procedure substantially improves run-time speed without affecting translation quality

Example-based machine translation using the marker hypothesis

The development of large-scale rules and grammars for a Rule-Based Machine Translation (RBMT) system is labour-intensive, error-prone and expensive. Current research in Machine Translation (MT) tends to focus on the development of corpus-based systems which can overcome the problem of knowledge acquisition. Corpus-Based Machine Translation (CBMT) can take the form of Statistical Machine Translation (SMT) or Example-Based Machine Translation (EBMT). Despite the benefits of EBMT, SMT is currently the dominant paradigm and many systems classified as example-based integrate additional rule-based and statistical techniques. The benefits of an EBMT system which does not require extensive linguistic resources and can produce reasonably intelligible and accurate translations cannot be overlooked. We show that our linguistics-lite EBMT system can outperform an SMT system trained on the same data. The work reported in this thesis describes the development of a linguistics-lite EBMT system which does not have recourse to extensive linguistic resources. We apply the Marker Hypothesis (Green, 1979) — a psycholinguistic theory which states that all natural languages are ‘marked’ for complex syntactic structure at surface form by a closed set of specific lexemes and morphemes. We use this technique in different environments to segment aligned (English, French) phrases and sentences. We then apply an alignment algorithm which can deduce smaller aligned chunks and words. Following a process similar to (Block, 2000), we generalise these alignments by replacing certain function words with an associated tag. In so doing, we cluster on marker words and add flexibility to our matching process. In a post hoc stage we treat the World Wide Web as a large corpus and validate and correct instances of determiner-noun and noun-verb boundary friction. We have applied our marker-based EBMT system to different bitexts and have explored its applicability in various environments. We have developed a phrase-based EBMT system (Gough et al., 2002; Way and Gough, 2003). We show that despite the perceived low quality of on-line MT systems, our EBMT system can produce good quality translations when such systems are used to seed its memories. (Carl, 2003a; Schaler et al., 2003) suggest that EBMT is more suited to controlled translation than RBMT as it has been known to overcome the ‘knowledge acquisition bottleneck’. To this end, we developed the first controlled EBMT system (Gough and Way, 2003; Way and Gough, 2004). Given the lack of controlled bitexts, we used an on-line MT system Logomedia to translate a set of controlled English sentences, We performed experiments using controlled analysis and generation and assessed the performance of our system at each stage. We made a number of improvements to our sub-sentential alignment algorithm and following some minimal adjustments to our system, we show that our controlled EBMT system can outperform an RBMT system. We applied the Marker Hypothesis to a more scalable data set. We trained our system on 203,529 sentences extracted from a Sun Microsystems Translation Memory. We thus reduced problems of data-sparseness and limited our dependence on Logomedia. We show that scaling up data in a marker-based EBMT system improves the quality of our translations. We also report on the benefits of extracting lexical equivalences from the corpus using Mutual Information

A Hybrid Machine Translation Framework for an Improved Translation Workflow

Over the past few decades, due to a continuing surge in the amount of content being translated and ever increasing pressure to deliver high quality and high throughput translation, translation industries are focusing their interest on adopting advanced technologies such as machine translation (MT), and automatic post-editing (APE) in their translation workflows. Despite the progress of the technology, the roles of humans and machines essentially remain intact as MT/APE are moving from the peripheries of the translation field closer towards collaborative human-machine based MT/APE in modern translation workflows. Professional translators increasingly become post-editors correcting raw MT/APE output instead of translating from scratch which in turn increases productivity in terms of translation speed. The last decade has seen substantial growth in research and development activities on improving MT; usually concentrating on selected aspects of workflows starting from training data pre-processing techniques to core MT processes to post-editing methods. To date, however, complete MT workflows are less investigated than the core MT processes. In the research presented in this thesis, we investigate avenues towards achieving improved MT workflows. We study how different MT paradigms can be utilized and integrated to best effect. We also investigate how different upstream and downstream component technologies can be hybridized to achieve overall improved MT. Finally we include an investigation into human-machine collaborative MT by taking humans in the loop. In many of (but not all) the experiments presented in this thesis we focus on data scenarios provided by low resource language settings.Aufgrund des stetig ansteigenden Übersetzungsvolumens in den letzten Jahrzehnten und gleichzeitig wachsendem Druck hohe Qualität innerhalb von kürzester Zeit liefern zu müssen sind Übersetzungsdienstleister darauf angewiesen, moderne Technologien wie Maschinelle Übersetzung (MT) und automatisches Post-Editing (APE) in den Übersetzungsworkflow einzubinden. Trotz erheblicher Fortschritte dieser Technologien haben sich die Rollen von Mensch und Maschine kaum verändert. MT/APE ist jedoch nunmehr nicht mehr nur eine Randerscheinung, sondern wird im modernen Übersetzungsworkflow zunehmend in Zusammenarbeit von Mensch und Maschine eingesetzt. Fachübersetzer werden immer mehr zu Post-Editoren und korrigieren den MT/APE-Output, statt wie bisher Übersetzungen komplett neu anzufertigen. So kann die Produktivität bezüglich der Übersetzungsgeschwindigkeit gesteigert werden. Im letzten Jahrzehnt hat sich in den Bereichen Forschung und Entwicklung zur Verbesserung von MT sehr viel getan: Einbindung des vollständigen Übersetzungsworkflows von der Vorbereitung der Trainingsdaten über den eigentlichen MT-Prozess bis hin zu Post-Editing-Methoden. Der vollständige Übersetzungsworkflow wird jedoch aus Datenperspektive weit weniger berücksichtigt als der eigentliche MT-Prozess. In dieser Dissertation werden Wege hin zum idealen oder zumindest verbesserten MT-Workflow untersucht. In den Experimenten wird dabei besondere Aufmertsamfit auf die speziellen Belange von sprachen mit geringen ressourcen gelegt. Es wird untersucht wie unterschiedliche MT-Paradigmen verwendet und optimal integriert werden können. Des Weiteren wird dargestellt wie unterschiedliche vor- und nachgelagerte Technologiekomponenten angepasst werden können, um insgesamt einen besseren MT-Output zu generieren. Abschließend wird gezeigt wie der Mensch in den MT-Workflow intergriert werden kann. Das Ziel dieser Arbeit ist es verschiedene Technologiekomponenten in den MT-Workflow zu integrieren um so einen verbesserten Gesamtworkflow zu schaffen. Hierfür werden hauptsächlich Hybridisierungsansätze verwendet. In dieser Arbeit werden außerdem Möglichkeiten untersucht, Menschen effektiv als Post-Editoren einzubinden

Zināšanās bāzētu un korpusā bāzētu metožu kombinētā izmantošanas mašīntulkošanā

ANOTĀCIJA. Mašīntulkošanas (MT) sistēmas tiek būvētas izmantojot dažādas metodes (zināšanās un korpusā bāzētas). Zināšanās bāzēta MT tulko tekstu, izmantojot cilvēka rakstītus likumus. Korpusā bāzēta MT izmanto no tulkojumu piemēriem automātiski izgūtus modeļus. Abām metodēm ir gan priekšrocības, gan trūkumi. Šajā darbā tiek meklēta kombināta metode MT kvalitātes uzlabošanai, kombinējot abas metodes. Darbā tiek pētīta metožu piemērotība latviešu valodai, kas ir maza, morfoloģiski bagāta valoda ar ierobežotiem resursiem. Tiek analizētas esošās metodes un tiek piedāvātas vairākas kombinētās metodes. Metodes ir realizētas un novērtētas, izmantojot gan automātiskas, gan cilvēka novērtēšanas metodes. Faktorēta statistiskā MT ar zināšanās balstītu morfoloģisko analizatoru ir piedāvāta kā perspektīvākā. Darbā aprakstīts arī metodes praktiskais pielietojums. Atslēgas vārdi: mašīntulkošana (MT), zināšanās balstīta MT, korpusā balstīta MT, kombinēta metodeABSTRACT. Machine Translation (MT) systems are built using different methods (knowledge-based and corpus-based). Knowledge-based MT translates text using human created rules. Corpus-based MT uses models which are automatically built from translation examples. Both methods have their advantages and disadvantages. This work aims to find a combined method to improve the MT quality combining both methods. An applicability of the methods for Latvian (a small, morphologically rich, under-resourced language) is researched. The existing MT methods have been analyzed and several combined methods have been proposed. Methods have been implemented and evaluated using an automatic and human evaluation. The factored statistical MT with a rule-based morphological analyzer is proposed to be the most promising. The practical application of methods is described. Keywords: Machine Translation (MT), Rule-based MT, Statistical MT, Combined approac

Language technologies for a multilingual Europe

This volume of the series “Translation and Multilingual Natural Language Processing” includes most of the papers presented at the Workshop “Language Technology for a Multilingual Europe”, held at the University of Hamburg on September 27, 2011 in the framework of the conference GSCL 2011 with the topic “Multilingual Resources and Multilingual Applications”, along with several additional contributions. In addition to an overview article on Machine Translation and two contributions on the European initiatives META-NET and Multilingual Web, the volume includes six full research articles. Our intention with this workshop was to bring together various groups concerned with the umbrella topics of multilingualism and language technology, especially multilingual technologies. This encompassed, on the one hand, representatives from research and development in the field of language technologies, and, on the other hand, users from diverse areas such as, among others, industry, administration and funding agencies. The Workshop “Language Technology for a Multilingual Europe” was co-organised by the two GSCL working groups “Text Technology” and “Machine Translation” (http://gscl.info) as well as by META-NET (http://www.meta-net.eu)

Language technologies for a multilingual Europe

This volume of the series “Translation and Multilingual Natural Language Processing” includes most of the papers presented at the Workshop “Language Technology for a Multilingual Europe”, held at the University of Hamburg on September 27, 2011 in the framework of the conference GSCL 2011 with the topic “Multilingual Resources and Multilingual Applications”, along with several additional contributions. In addition to an overview article on Machine Translation and two contributions on the European initiatives META-NET and Multilingual Web, the volume includes six full research articles. Our intention with this workshop was to bring together various groups concerned with the umbrella topics of multilingualism and language technology, especially multilingual technologies. This encompassed, on the one hand, representatives from research and development in the field of language technologies, and, on the other hand, users from diverse areas such as, among others, industry, administration and funding agencies. The Workshop “Language Technology for a Multilingual Europe” was co-organised by the two GSCL working groups “Text Technology” and “Machine Translation” (http://gscl.info) as well as by META-NET (http://www.meta-net.eu)

Idiom treatment experiments in machine translation

Idiomatic expressions pose a particular challenge for the today\u27;s Machine Translation systems, because their translation mostly does not result literally, but logically. The present dissertation shows, how with the help of a corpus, and morphosyntactic rules, such idiomatic expressions can be recognized and finally correctly translated. The work leads the reader in the first chapter generally to the field of Machine Translation and following that, it focuses on the special field of Example-based Machine Translation. Next, an important part of the doctoral thesis dissertation is devoted to the theory of idiomatic expressions. The practical part of the thesis describes how the hybrid Example-based Machine Translation system METIS-II, with the help of morphosyntactic rules, is able to correctly process certain idiomatic expressions and finally, to translate them. The following chapter deals with the function of the transfer system CAT2 and its handling of the idiomatic expressions. The last part of the thesis includes the evaluation of three commercial systems, namely SYSTRAN, T1 Langenscheidt, and Power Translator Pro, with respect to continuous and discontinuous idiomatic expressions. For this, both small corpora and a part of the extensive corpus Europarl and the Digital Lexicon of the German Language in 20th century were processed, firstly manually and then automatically. The dissertation concludes with results from this evaluation.Idiomatische Redewendungen stellen für heutige maschinelle Übersetzungssysteme eine besondere Herausforderung dar, da ihre Übersetzung nicht wörtlich, sondern stets sinngemäß erfolgen muss. Die vorliegende Dissertation zeigt, wie mit Hilfe eines Korpus sowie morphosyntaktischer Regeln solche idiomatische Redewendungen erkannt und am Ende richtig übersetzt werden können. Die Arbeit führt den Leser im ersten Kapitel allgemein in das Gebiet der Maschinellen Übersetzung ein und vertieft im Anschluss daran das Spezialgebiet der Beispielbasierten Maschinellen Übersetzung. Im Folgenden widmet sich ein wesentlicher Teil der Doktorarbeit der Theorie über idiomatische Redewendungen. Der praktische Teil der Arbeit beschreibt wie das hybride Beispielbasierte Maschinelle Übersetzungssystem METIS-II mit Hilfe von morphosyntaktischen Regeln befähigt wurde, bestimmte idiomatische Redewendungen korrekt zu bearbeiten und am Ende zu übersetzen. Das nachfolgende Kapitel behandelt die Funktion des Transfersystems CAT2 und dessen Umgang mit idiomatischen Wendungen. Der letzte Teil der Arbeit beinhaltet die Evaluation von drei kommerzielle Systemen, nämlich SYSTRAN, T1 Langenscheidt und Power Translator Pro, in Bezug auf deren Umgang mit kontinuierlichen und diskontinuierlichen idiomatischen Redewendungen. Hierzu wurden sowohl kleine Korpora als auch ein Teil des umfangreichen Korpus Europarl und des Digatalen Wörterbuchs der deutschen Sprache des 20. Jh. erst manuell und dann maschinell bearbeitet. Die Dissertation wird mit Folgerungen aus der Evaluation abgeschlossen

Multi-word unit processing in machine translation. Developing and using language resources for multi-word unit processing in machine translation

2011 - 2012XI n.s

TC3 III

This volume of the series “Translation and Multilingual Natural Language Processing” includes most of the papers presented at the Workshop “Language Technology for a Multilingual Europe”, held at the University of Hamburg on September 27, 2011 in the framework of the conference GSCL 2011 with the topic “Multilingual Resources and Multilingual Applications”, along with several additional contributions. In addition to an overview article on Machine Translation and two contributions on the European initiatives META-NET and Multilingual Web, the volume includes six full research articles. Our intention with this workshop was to bring together various groups concerned with the umbrella topics of multilingualism and language technology, especially multilingual technologies. This encompassed, on the one hand, representatives from research and development in the field of language technologies, and, on the other hand, users from diverse areas such as, among others, industry, administration and funding agencies. The Workshop “Language Technology for a Multilingual Europe” was co-organised by the two GSCL working groups “Text Technology” and “Machine Translation” (http://gscl.info) as well as by META-NET (http://www.meta-net.eu)

A generalised alignment template formalism and its application to the inference of shallow-transfer machine translation rules from scarce bilingual corpora

Statistical and rule-based methods are complementary approaches to machine translation (MT) that have different strengths and weaknesses. This complementarity has, over the last few years, resulted in the consolidation of a growing interest in hybrid systems that combine both data-driven and linguistic approaches. In this paper, we address the situation in which the amount of bilingual resources that is available for a particular language pair is not sufficiently large to train a competitive statistical MT system, but the cost and slow development cycles of rule-based MT systems cannot be afforded either. In this context, we formalise a new method that uses scarce parallel corpora to automatically infer a set of shallow-transfer rules to be integrated into a rule-based MT system, thus avoiding the need for human experts to handcraft these rules. Our work is based on the alignment template approach to phrase-based statistical MT, but the definition of the alignment template is extended to encompass different generalisation levels. It is also greatly inspired by the work of Sánchez-Martínez and Forcada (2009) in which alignment templates were also considered for shallow-transfer rule inference. However, our approach overcomes many relevant limitations of that work, principally those related to the inability to find the correct generalisation level for the alignment templates, and to select the subset of alignment templates that ensures an adequate segmentation of the input sentences by the rules eventually obtained. Unlike previous approaches in literature, our formalism does not require linguistic knowledge about the languages involved in the translation. Moreover, it is the first time that conflicts between rules are resolved by choosing the most appropriate ones according to a global minimisation function rather than proceeding in a pairwise greedy fashion. Experiments conducted using five different language pairs with the free/open-source rule-based MT platform Apertium show that translation quality significantly improves when compared to the method proposed by Sánchez-Martínez and Forcada (2009), and is close to that obtained using handcrafted rules. For some language pairs, our approach is even able to outperform them. Moreover, the resulting number of rules is considerably smaller, which eases human revision and maintenance.Research funded by Universitat d’Alacant through project GRE11-20, by the Spanish Ministry of Economy and Competitiveness through projects TIN2009-14009-C02-01 and TIN2012-32615, by Generalitat Valenciana through grant ACIF/2010/174, and by the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement PIAP-GA-2012-324414 (Abu-MaTran)