TÜBİTAK SMT System Submission for WMT2016

Bektaş, Emre; Yilmaz, Ertugrul; Mermer, Coskun; El-Kahlout, Ilknur Durgar

doi:10.18653/v1/w16-2305

Cited by 2 publications

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References 9 publications

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“…In the preprocessing before MT training, several studies (Oflazer and Durgar El-Kahlout 2007;Durgar El-Kahlout and Oflazer 2010;Bektaş et al 2016) suggest using a morphological analyzer to analyze the root and morphemes in the Turkish corpus and make a morpheme alignment between Turkish and English sentences.…”

Section: 1mentioning

confidence: 99%

Statistical Machine Translation Customization between Turkish and 11 Languages

Doğru¹

2020

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Statistical Machine Translation (SMT) has been the dominant corpus-based machine translation (MT) approach in the last twenty years. While SMT has been studied in detail among European languages, it has not been studied sufficiently in language pairs including Turkish as source or target language, and its study has been limited mostly to English ↔ Turkish language pair. This study aims to broaden the perspective on Turkish corpus-based MT studies by training MT engines between Turkish and a wide variety of languages with different features. It surveys customized SMT between Turkish and 11 different languages. Twenty-two SMT engines have been trained in KantanMT with open parallel corpora using Turkish as both source and target language. Three automatic evaluation metrics F-Measure, BLEU, and TER have been used for evaluating MT quality. Due to the variations in the corpus quality and size, highly varying results have been achieved. While Turkish ↔ Catalan engines have had the highest automatic evaluation scores, Turkish ↔ Arabic engines have had the lowest automatic scores. While the quality results are highly varying across languages, we obtain baseline scores for a wide variety of languages coupled with Turkish. These results may provide a reference point for evaluating future MT systems including Turkish.

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Section: 1mentioning

confidence: 99%

Statistical Machine Translation Customization between Turkish and 11 Languages

Doğru¹

2020

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Morphologically Motivated Input Variations and Data Augmentation in Turkish-English Neural Machine Translation

Yirmibeşoğlu

Güngör

2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Success of neural networks in natural language processing has paved the way for neural machine translation (NMT), which rapidly became the mainstream approach in machine translation. Significant improvement in translation performance has been achieved with breakthroughs such as encoder-decoder networks, attention mechanism, and Transformer architecture. However, the necessity of large amounts of parallel data for training an NMT system and rare words in translation corpora are issues yet to be overcome. In this article, we approach neural machine translation of the low-resource Turkish-English language pair. We employ state-of-the-art NMT architectures and data augmentation methods that exploit monolingual corpora. We point out the importance of input representation for the morphologically-rich Turkish language and make a comprehensive analysis of linguistically and non-linguistically motivated input segmentation approaches. We prove the effectiveness of morphologically motivated input segmentation for the Turkish language. Moreover, we show the superiority of the Transformer architecture over attentional encoder-decoder models for the Turkish-English language pair. Among the employed data augmentation approaches, we observe back-translation to be the most effective and confirm the benefit of increasing the amount of parallel data on translation quality. This research demonstrates a comprehensive analysis on NMT architectures with different hyperparameters, data augmentation methods, and input representation techniques, and proposes ways of tackling the low-resource setting of Turkish-English NMT.

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TÜBİTAK SMT System Submission for WMT2016

Cited by 2 publications

References 9 publications

Statistical Machine Translation Customization between Turkish and 11 Languages

Statistical Machine Translation Customization between Turkish and 11 Languages

Morphologically Motivated Input Variations and Data Augmentation in Turkish-English Neural Machine Translation

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