Arda Tezcan


2020

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Assessing the Comprehensibility of Automatic Translations (ArisToCAT)
Lieve Macken | Margot Fonteyne | Arda Tezcan | Joke Daems
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

The ArisToCAT project aims to assess the comprehensibility of ‘raw’ (unedited) MT output for readers who can only rely on the MT output. In this project description, we summarize the main results of the project and present future work.

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Literary Machine Translation under the Magnifying Glass: Assessing the Quality of an NMT-Translated Detective Novel on Document Level
Margot Fonteyne | Arda Tezcan | Lieve Macken
Proceedings of the 12th Language Resources and Evaluation Conference

Several studies (covering many language pairs and translation tasks) have demonstrated that translation quality has improved enormously since the emergence of neural machine translation systems. This raises the question whether such systems are able to produce high-quality translations for more creative text types such as literature and whether they are able to generate coherent translations on document level. Our study aimed to investigate these two questions by carrying out a document-level evaluation of the raw NMT output of an entire novel. We translated Agatha Christie’s novel The Mysterious Affair at Styles with Google’s NMT system from English into Dutch and annotated it in two steps: first all fluency errors, then all accuracy errors. We report on the overall quality, determine the remaining issues, compare the most frequent error types to those in general-domain MT, and investigate whether any accuracy and fluency errors co-occur regularly. Additionally, we assess the inter-annotator agreement on the first chapter of the novel.

2019

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When a ‘sport’ is a person and other issues for NMT of novels
Arda Tezcan | Joke Daems | Lieve Macken
Proceedings of the Qualities of Literary Machine Translation

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Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation
Bram Bulte | Arda Tezcan
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We present a simple yet powerful data augmentation method for boosting Neural Machine Translation (NMT) performance by leveraging information retrieved from a Translation Memory (TM). We propose and test two methods for augmenting NMT training data with fuzzy TM matches. Tests on the DGT-TM data set for two language pairs show consistent and substantial improvements over a range of baseline systems. The results suggest that this method is promising for any translation environment in which a sizeable TM is available and a certain amount of repetition across translations is to be expected, especially considering its ease of implementation.

2018

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A fine-grained error analysis of NMT, SMT and RBMT output for English-to-Dutch
Laura Van Brussel | Arda Tezcan | Lieve Macken
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2016

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UGENT-LT3 SCATE Submission for WMT16 Shared Task on Quality Estimation
Arda Tezcan | Véronique Hoste | Lieve Macken
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Detecting Grammatical Errors in Machine Translation Output Using Dependency Parsing and Treebank Querying
Arda Tezcan | Veronique Hoste | Lieve Macken
Proceedings of the 19th Annual Conference of the European Association for Machine Translation

2015

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Smart Computer Aided Translation Environment
Vincent Vandeghinste | Tom Vanallemeersch | Frank Van Eynde | Geert Heyman | Sien Moens | Joris Pelemans | Patrick Wambacq | Iulianna Van der Lek - Ciudin | Arda Tezcan | Lieve Macken | Véronique Hoste | Eva Geurts | Mieke Haesen
Proceedings of the 18th Annual Conference of the European Association for Machine Translation

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UGENT-LT3 SCATE System for Machine Translation Quality Estimation
Arda Tezcan | Veronique Hoste | Bart Desmet | Lieve Macken
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Smart Computer Aided Translation Environment - SCATE
Vincent Vandeghinste | Tom Vanallemeersch | Frank Van Eynde | Geert Heyman | Sien Moens | Joris Pelemans | Patrick Wambacq | Iulianna Van der Lek - Ciudin | Arda Tezcan | Lieve Macken | Véronique Hoste | Eva Geurts | Mieke Haesen
Proceedings of the 18th Annual Conference of the European Association for Machine Translation

2011

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SMT-CAT integration in a Technical Domain: Handling XML Markup Using Pre & Post-processing Methods
Arda Tezcan | Vincent Vandeghinste
Proceedings of the 15th Annual conference of the European Association for Machine Translation