Statistical machine translation (SMT) requires large quantities of bitexts (i.e., bilingual parallel corpora) as training data to yield good quality translations. While obtaining a large amount of training data is critical, the similarity between training and test data also has a significant impact on SMT performance. Many SMT studies define data similarity in terms of domain-overlap, and domains are defined to be synonymous with data sources. Consequently, the SMT community has focused on domain adaptation techniques that augment small (in-domain) datasets with large datasets from other sources (hence, out-of-domain, per the definition). However, many training datasets consist of topically diverse data, and not all data contained in a single dataset are useful for translations of a specific target task. In this study, we propose a new perspective on data quality and topical similarity to enhance SMT performance. Using our data adaptation approach called topic adaptation, we select topically suitable training data corresponding to test data in order to produce better translations. We propose three topic adaptation approaches for the SMT process and investigate the effectiveness in both idealized and realistic settings using large parallel corpora. We measure performance of SMT systems trained on topically similar data and their effectiveness based on BLEU, the widely-used objective SMT performance metric. We show that topic adaptation approaches outperform baseline systems (0.3 – 3 BLEU points) when data selection parameters are carefully determined.

This report introduces an implemented scheme for generating target- language sentences using a compositional representation of meaning called lexical conceptual structure. Lexical conceptual structure facilitates two crucial operations associated with generation: lexical selection and syntactic realization. The compositional nature of the representation is particularly valuable for these two operations when semantically equivalent source-and-target-language words and phrases are structurally or thematically divergent. To determine the correct lexical items and syntactic realization associated with the surface form in such cases, the underlying lexical-semantic forms are systematically mapped to the target-language syntactic structures. The model described constitutes a lexical-semantic extension to UNITRAN.

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This thesis investigates the application of structured sequence classification models to multilingual natural language processing (NLP). Many tasks tackled by NLP can be framed as classification, where we seek to assign a label to a particular piece of text, be it a word, sentence or document. Yet often the labels which we’d like to assign exhibit complex internal structure, such as labelling a sentence with its parse tree, and there may be an exponential number of them to choose from. Structured classification seeks to exploit the structure of the labels in order to allow both generalisation across labels which differ by only a small amount, and tractable searches over all possible labels. In this thesis we focus on the application of conditional random field (CRF) models (Lafferty et al., 2001). These models assign an undirected graphical structure to the labels of the classification task and leverage dynamic programming algorithms to efficiently identify the optimal label for a given input. We develop a range of models for two multilingual NLP applications: word-alignment for statistical machine translation (SMT), and multilingual super tagging for highly lexicalised grammars.

Machine translation translates a text from one language to another, while text simplification converts a text from its original form to a simpler one, usually in the same language. This survey paper discusses the evaluation (manual and automatic) of both fields, providing an overview of existing metrics along with their strengths and weaknesses. The first chapter takes an in-depth look at machine translation evaluation metrics, namely BLEU, NIST, AMBER, LEPOR, MP4IBM1, TER, MMS, METEOR, TESLA, RTE, and HTER. The second chapter focuses more generally on text simplification, starting with a discussion of the theoretical underpinnings of the field (i.e what ``simple'' means). Then, an overview of automatic evaluation metrics, namely BLEU and Flesch-Kincaid, is given, along with common approaches to text simplification. The paper concludes with a discussion of the future trajectory of both fields.

The aim of this thesis is to explore Machine Translation and the problems that these system are experiencing when translation between two different languages. The grammatical structures will be studied for English, Swedish and Persian to find a common pattern that could relate different ideas in each language to each other. In the other hand an inter lingual MT will be developed according to “René Descartes” principals that not only produces translations to English, Persian and Swedish but it even provides a new way of inputting text just by clicking buttons which each represent a word or concept. Then the system will be presented to a group of chosen users to study the human interaction with the application and identifying new problems associated with the new developed system and evaluating the results. The specific objectives are: the role of prepositions and other grammatical structures in determining the meaning of a text. The study even examines the possibility of using Descartes theory for improving Machine Translation. The study was conducted in “BTH”. The data was collected through research, experiments, and Self-reporting. / bamdadpayvar@msn.com

In the context of an increasingly networked world, the availability of high quality translations is critical for success in the context of the growing international competition. Large international companies as well as medium sized companies are required to provide well translated, high quality technical documentation for their customers not only to be successful in the market but also to meet legal regulations and to avoid lawsuits. Therefore, this thesis focuses on the evaluation of translation quality, specifically concerning technical documentation, and answers two central questions: How can the translation quality of technical documents be evaluated, given the original document is available? How can the translation quality of technical documents be evaluated, given the original document is not available? These questions are answered using state-of-the-art machine learning algorithms and translation evaluation metrics in the context of a knowledge discovery process. The evaluations are done on a sentence level and recombined on a document level by binarily classifying sentences as automated translation and professional translation. The research is based on a database containing 22, 327 sentences and 32 translation evaluation attributes, which are used for optimizations of five different machine learning approaches. An optimization process consisting of 795, 000 evaluations shows a prediction accuracy of up to 72.24% for the binary classification. Based on the developed sentence-based classifi- cation systems, documents are classified using recombination of the affiliated sentences and a framework for rating document quality is introduced. Therefore, the taken approach successfully creates a classification and evaluation system.