This paper introduces the work on building a machine translation system for Arabic-to-Turkish in the news domain. Our work includes collecting parallel datasets in several ways for a new and low-resourced language pair, building baseline systems with state-of-the-art architectures and developing language specific algorithms for better translation. Parallel datasets are mainly collected three different ways; i) translating Arabic texts into Turkish by professional translators, ii) exploiting the web for open-source Arabic-Turkish parallel texts, iii) using back-translation. We per-formed preliminary experiments for Arabic-to-Turkish machine translation with neural(Marian) machine translation tools with a novel morphologically motivated vocabulary reduction method.

WedescribetheTU ̈B ̇ITAKsubmissiontotheIWSLT2012 Evaluation Campaign. Our system development focused on utilizing Bayesian alignment methods such as variational Bayes and Gibbs sampling in addition to the standard GIZA++ alignments. The submitted tracks are the Arabic-English and Turkish-English TED Talks translation tasks.

Paraphrases are alternative syntactic forms in the same language expressing the same semantic content. Speakers of all languages are inherently familiar with paraphrases at different levels of granularity (lexical, phrasal, and sentential). For quite some time, the concept of paraphrasing is getting a growing attention by the research community and its potential use in several natural language processing applications (such as text summarization and machine translation) is being investigated. In this paper, we present, what is to our best knowledge, the first Turkish paraphrase corpus. The corpus is gleaned from four different sources and currently contains 1270 paraphrase pairs. All paraphrase pairs are carefully annotated by native Turkish speakers with the identified semantic correspondences between paraphrases. The work for expanding the corpus is still under way.

We report on our participation in the IWSLT 2010 evaluation campaign. Similar to previous years, our submitted systems are based on the Moses statistical machine translation toolkit. This year, we also experimented with hierarchical phrase-based models. In addition, we utilized automatic minimum error-rate training instead of manually-guided tuning. We focused more on the BTEC Turkish-English task and explored various experimentations with unsupervised segmentation to measure their effects on the translation performance. We present the results of several contrastive experiments, including those that failed to improve the translation performance.

We describe our Arabic-to-English and Turkish-to-English machine translation systems that participated in the IWSLT 2009 evaluation campaign. Both systems are based on the Moses statistical machine translation toolkit, with added components to address the rich morphology of the source languages. Three different morphological approaches are investigated for Turkish. Our primary submission uses linguistic morphological analysis and statistical disambiguation to generate morpheme-based translation models, which is the approach with the better translation performance. One of the contrastive submissions utilizes unsupervised subword segmentation to generate non-linguistic subword-based translation models, while another contrastive system uses word-based models but makes use of lexical approximation to cope with out-of-vocabulary words, similar to the approach in our Arabic-to-English submission.

We present the TÜBİTAK-UEKAE statistical machine translation system that participated in the IWSLT 2008 evaluation campaign. Our system is based on the open-source phrase-based statistical machine translation software Moses. Additionally, phrase-table augmentation is applied to maximize source language coverage; lexical approximation is applied to replace out-of-vocabulary words with known words prior to decoding; and automatic punctuation insertion is improved. We describe the preprocessing and postprocessing steps and our training and decoding procedures. Results are presented on our participation in the classical Arabic-English and Chinese-English tasks as well as the new Chinese-Spanish direct and Chinese-English-Spanish pivot translation tasks.

We describe the TÜBITAK-UEKAE system that participated in the Arabic-to-English and Japanese-to-English translation tasks of the IWSLT 2007 evaluation campaign. Our system is built on the open-source phrase-based statistical machine translation software Moses. Among available corpora and linguistic resources, only the supplied training data and an Arabic morphological analyzer are used in the system. We present the run-time lexical approximation method to cope with out-of-vocabulary words during decoding. We tested our system under both automatic speech recognition (ASR) and clean transcript (clean) input conditions. Our system was ranked first in both Arabic-to-English and Japanese-to-English tasks under the “clean” condition.