Saša Hasan

Also published as: Sasa Hasan


2016

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Models and Inference for Prefix-Constrained Machine Translation
Joern Wuebker | Spence Green | John DeNero | Saša Hasan | Minh-Thang Luong
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Spelling Correction of User Search Queries through Statistical Machine Translation
Saša Hasan | Carmen Heger | Saab Mansour
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2009

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Comparison of Extended Lexicon Models in Search and Rescoring for SMT
Saša Hasan | Hermann Ney
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

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Extending Statistical Machine Translation with Discriminative and Trigger-Based Lexicon Models
Arne Mauser | Saša Hasan | Hermann Ney
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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A Deep Learning Approach to Machine Transliteration
Thomas Deselaers | Saša Hasan | Oliver Bender | Hermann Ney
Proceedings of the Fourth Workshop on Statistical Machine Translation

2008

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A Multi-Genre SMT System for Arabic to French
Saša Hasan | Hermann Ney
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This work presents improvements of a large-scale Arabic to French statistical machine translation system over a period of three years. The development includes better preprocessing, more training data, additional genre-specific tuning for different domains, namely newswire text and broadcast news transcripts, and improved domain-dependent language models. Starting with an early prototype in 2005 that participated in the second CESTA evaluation, the system was further upgraded to achieve favorable BLEU scores of 44.8% for the text and 41.1% for the audio setting. These results are compared to a system based on the freely available Moses toolkit. We show significant gains both in terms of translation quality (up to +1.2% BLEU absolute) and translation speed (up to 16 times faster) for comparable configuration settings.

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Automatic Evaluation Measures for Statistical Machine Translation System Optimization
Arne Mauser | Saša Hasan | Hermann Ney
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Evaluation of machine translation (MT) output is a challenging task. In most cases, there is no single correct translation. In the extreme case, two translations of the same input can have completely different words and sentence structure while still both being perfectly valid. Large projects and competitions for MT research raised the need for reliable and efficient evaluation of MT systems. For the funding side, the obvious motivation is to measure performance and progress of research. This often results in a specific measure or metric taken as primarily evaluation criterion. Do improvements in one measure really lead to improved MT performance? How does a gain in one evaluation metric affect other measures? This paper is going to answer these questions by a number of experiments.

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Triplet Lexicon Models for Statistical Machine Translation
Saša Hasan | Juri Ganitkevitch | Hermann Ney | Jesús Andrés-Ferrer
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2007

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A Systematic Comparison of Training Criteria for Statistical Machine Translation
Richard Zens | Saša Hasan | Hermann Ney
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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Are Very Large N-Best Lists Useful for SMT?
Saša Hasan | Richard Zens | Hermann Ney
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

2006

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A Flexible Architecture for CAT Applications
Saša Hasan | Shahram Khadivi | Richard Zens | Hermann Ney
Proceedings of the 11th Annual conference of the European Association for Machine Translation

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Creating a Large-Scale Arabic to French Statistical MachineTranslation System
Saša Hasan | Anas El Isbihani | Hermann Ney
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

In this work, the creation of a large-scale Arabic to French statistical machine translation system is presented. We introduce all necessary steps from corpus aquisition, preprocessing the data to training and optimizing the system and eventual evaluation. Since no corpora existed previously, we collected large amounts of data from the web. Arabic word segmentation was crucial to reduce the overall number of unknown words. We describe the phrase-based SMT system used for training and generation of the translation hypotheses. Results on the second CESTA evaluation campaign are reported. The setting was inthe medical domain. The prototype reaches a favorable BLEU score of40.8%.

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The RWTH statistical machine translation system for the IWSLT 2006 evaluation
Arne Mauser | Richard Zens | Evgeny Matusov | Sasa Hasan | Hermann Ney
Proceedings of the Third International Workshop on Spoken Language Translation: Evaluation Campaign

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Reranking Translation Hypotheses Using Structural Properties
Saša Hasan | Oliver Bender | Hermann Ney
Proceedings of the Workshop on Learning Structured Information in Natural Language Applications

2005

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Statistical Machine Translation of European Parliamentary Speeches
David Vilar | Evgeny Matusov | Sasa Hasan | Richard Zens | Hermann Ney
Proceedings of Machine Translation Summit X: Papers

In this paper we present the ongoing work at RWTH Aachen University for building a speech-to-speech translation system within the TC-Star project. The corpus we work on consists of parliamentary speeches held in the European Plenary Sessions. To our knowledge, this is the first project that focuses on speech-to-speech translation applied to a real-life task. We describe the statistical approach used in the development of our system and analyze its performance under different conditions: dealing with syntactically correct input, dealing with the exact transcription of speech and dealing with the (noisy) output of an automatic speech recognition system. Experimental results show that our system is able to perform adequately in each of these conditions.

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The RWTH Phrase-based Statistical Machine Translation System
Richard Zens | Oliver Bender | Sasa Hasan | Shahram Khadivi | Evgeny Matusov | Jia Xu | Yuqi Zhang | Hermann Ney
Proceedings of the Second International Workshop on Spoken Language Translation

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Comparison of generation strategies for interactive machine translation
Oliver Bender | Saša Hasan | David Vilar | Richard Zens | Hermann Ney
Proceedings of the 10th EAMT Conference: Practical applications of machine translation

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Clustered language models based on regular expressions for SMT
Saša Hasan | Hermann Ney
Proceedings of the 10th EAMT Conference: Practical applications of machine translation

2004

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N-Best Hidden Markov Model Supertagging to Improve Typing on an Ambiguous Keyboard
Saša Hasan | Karin Harbusch
Proceedings of the 7th International Workshop on Tree Adjoining Grammar and Related Formalisms

2003

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Domain-Specific Disambiguation for Typing with Ambiguous Keyboards
Karin Harbusch | Sasa Hasan | Hajo Hoffmann | Michael Kühn | Bernhard Schüler
Proceedings of the 2003 EACL Workshop on Language Modeling for Text Entry Methods