Quantifying Cross-lingual Semantic Similarity for Natural Language Processing Applications

Translation and cross-lingual access to information are key technologies in a global economy. Even though the quality of machine translation (MT) output is still far from the level of human translations, many real-world applications have emerged, for which MT can be employed. Machine translation supports human translators in computer-assisted translation (CAT), providing the opportunity to improve translation systems based on human interaction and feedback. Besides, many tasks that involve natural language processing operate in a cross-lingual setting, where there is no need for perfectly fluent translations and the transfer of meaning can be modeled by employing MT technology. This thesis describes cumulative work in the field of cross-lingual natural language processing in a user-oriented setting. A common denominator of the presented approaches is their anchoring in an alignment between texts in two different languages to quantify the similarity of their content

Terminology Integration in Statistical Machine Translation

Elektroniskā versija nesatur pielikumusPromocijas darbs apraksta autora izpētītas metodes un izstrādātus rīkus divvalodu terminoloģijas integrācijai statistiskās mašīntulkošanas sistēmās. Autors darbā piedāvā inovatīvas metodes terminu integrācijai SMT sistēmu trenēšanas fāzē (ar statiskas integrācijas palīdzību) un tulkošanas fāzē (ar dinamiskas integrācijas palīdzību). Darbā uzmanība pievērsta ne tikai metodēm terminu integrācijai SMT, bet arī metodēm valodas resursu, kas nepieciešami dažādu uzdevumu veikšanai terminu integrācijas SMT darbplūsmās, ieguvei. Piedāvātās metodes ir novērtētas automātiskas un manuālas novērtēšanas eksperimentos. Iegūtie rezultāti parāda, ka statiskās un dinamiskās integrācijas metodes ļauj būtiski uzlabot tulkošanas kvalitāti. Darbā aprakstītie rezultāti ir aprobēti vairākos pētniecības projektos un ieviesti praktiskos risinājumos. Atslēgvārdi: statistiskā mašīntulkošana, terminoloģija, starpvalodu informācijas izvilkšanaThe doctoral thesis describes methods and tools researched and developed by the author for bilingual terminology integration into statistical machine translation systems. The author presents novel methods for terminology integration in SMT systems during training (through static integration) and during translation (through dynamic integration). The work focusses not only on the SMT integration techniques, but also on methods for acquisition of linguistic resources that are necessary for different tasks involved in workflows for terminology integration in SMT systems. The proposed methods have been evaluated using automatic and manual evaluation methods. The results show that both static and dynamic integration methods allow increasing translation quality. The thesis describes also areas where the methods have been approbated in practice. Keywords: statistical machine translation, terminology, cross-lingual information extractio

Dynamic Models in Moses for Online Adaptation

Abstract A very hot issue for research and industry is how to effectively integrate machine translation (MT) within computer assisted translation (CAT) software. This paper focuses on this issue, and more generally how to dynamically adapt phrase-based statistical machine translation (SMT) by exploiting external knowledge, like the post-editions from professional translators. We present an enhancement of the Moses SMT toolkit dynamically adaptable to external information, which becomes available during the translation process, and which can depend on the previously translated text. We have equipped Moses with two new elements: a new phrase table implementation and a new LM-like feature. Both the phrase table and the LM-like feature can be dynamically modified by adding and removing entries and re-scoring them according to a time-decaying scoring function. The final goal of these two dynamically adaptable features is twofold: to create additional translation alternatives and to reward those which are composed of entries previously inserted therein. The implemented dynamic system is highly configurable, flexible and applicable to many tasks, like for instance online MT adaptation, interactive MT, and context-aware MT. When exploited in a real-world CAT scenario where online adaptation is applied to repetitive texts, it has proven itself very effective in improving translation quality and reducing post-editing effort

Dynamic Models in Moses for Online Adaptation

A very hot issue for research and industry is how to effectively integrate machine translation (MT) within computer assisted translation (CAT) software. This paper focuses on this issue, and more generally how to dynamically adapt phrase-based statistical machine translation (SMT) by exploiting external knowledge, like the post-editions from professional translators. We present an enhancement of the Moses SMT toolkit dynamically adaptable to external information, which becomes available during the translation process, and which can depend on the previously translated text. We have equipped Moses with two new elements: a new phrase table implementation and a new LM-like feature. Both the phrase table and the LM-like feature can be dynamically modified by adding and removing entries and re-scoring them according to a time-decaying scoring function. The final goal of these two dynamically adaptable features is twofold: to create additional translation alternatives and to reward those which are composed of entries previously inserted therein. The implemented dynamic system is highly configurable, flexible and applicable to many tasks, like for instance online MT adaptation, interactive MT, and context-aware MT. When exploited in a real-world CAT scenario where online adaptation is applied to repetitive texts, it has proven itself very effective in improving translation quality and reducing post-editing effort