Necip Fazil Ayan

Also published as: Necip Fazil Ayan


2008

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Improving Alignments for Better Confusion Networks for Combining Machine Translation Systems
Necip Fazil Ayan | Jing Zheng | Wen Wang
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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Combining Outputs from Multiple Machine Translation Systems
Antti-Veikko Rosti | Necip Fazil Ayan | Bing Xiang | Spyros Matsoukas | Richard Schwartz | Bonnie Dorr
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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Using Paraphrases for Parameter Tuning in Statistical Machine Translation
Nitin Madnani | Necip Fazil Ayan | Philip Resnik | Bonnie Dorr
Proceedings of the Second Workshop on Statistical Machine Translation

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Measuring Variability in Sentence Ordering for News Summarization
Nitin Madnani | Rebecca Passonneau | Necip Fazil Ayan | John Conroy | Bonnie Dorr | Judith Klavans | Dianne O’Leary | Judith Schlesinger
Proceedings of the Eleventh European Workshop on Natural Language Generation (ENLG 07)

2006

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Going Beyond AER: An Extensive Analysis of Word Alignments and Their Impact on MT
Necip Fazil Ayan | Bonnie J. Dorr
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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A Maximum Entropy Approach to Combining Word Alignments
Necip Fazil Ayan | Bonnie J. Dorr
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

2005

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NeurAlign: Combining Word Alignments Using Neural Networks
Necip Fazil Ayan | Bonnie J. Dorr | Christof Monz
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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Alignment Link Projection Using Transformation-Based Learning
Necip Fazil Ayan | Bonnie J. Dorr | Christof Monz
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

2004

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Multi-Align: combining linguistic and statistical techniques to improve alignments for adaptable MT
Necip Fazil Ayan | Bonnie Dorr | Nizar Habash
Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers

An adaptable statistical or hybrid MT system relies heavily on the quality of word-level alignments of real-world data. Statistical alignment approaches provide a reasonable initial estimate for word alignment. However, they cannot handle certain types of linguistic phenomena such as long-distance dependencies and structural differences between languages. We address this issue in Multi-Align, a new framework for incremental testing of different alignment algorithms and their combinations. Our design allows users to tune their systems to the properties of a particular genre/domain while still benefiting from general linguistic knowledge associated with a language pair. We demonstrate that a combination of statistical and linguistically-informed alignments can resolve translation divergences during the alignment process.

2003

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Evaluation techniques applied to domain tuning of MT lexicons
Necip Fazil Ayan | Bonnie J. Dorr | Okan Kolak
Workshop on Systemizing MT Evaluation