OPUS-CAT: Desktop NMT with CAT integration and local fine-tuning

Nieminen, Tommi

doi:10.18653/v1/2021.eacl-demos.34

Cited by 4 publications

(4 citation statements)

References 9 publications

(8 reference statements)

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“…The first type of engine is a pretrained model from OPUS-MT (Tiedemann and Thottingal 2020). It was downloaded to OPUS-CAT (Nieminen 2021) through the built-in feature "Install OPUS Model from Web". Once the download was complete the engine was ready for translation.…”

Section: Methodology and Research Designmentioning

confidence: 99%

“…The convergence of different projects within the OPUS platform (Tiedemann et al 2022) such as OPUS Corpus (Tiedemann 2012), OPUS-MT (Tiedemann and Thottingal 2020) and OPUS-CAT (Nieminen 2021) has, among other things, paved the way for translators to use MT in different ways in their workflow. The release of OPUS-CAT has particularly bridged the gap between MT research and professional use of MT by translators.…”

Section: Related Workmentioning

confidence: 99%

“…Since these NMT systems run locally, they can keep translated data secure to avoid it leaking externally, and being customizable, engines can be fine-tuned to potentially improve translation quality and consistency by adapting to the translation memories (TMs) of the translators. We use OPUS-CAT (Nieminen 2021), a software collection that provides these capabilities through pretrained NMT models (Tiedemann & Thottingal 2020) and a fine-tuning feature, with plugins to integrate with many CAT tools including OmegaT 1 , Trados 2 and memoQ 3 .…”

Section: Introductionmentioning

confidence: 99%

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Data Augmentation with Translation Memories for Desktop Machine Translation Fine-tuning in 3 Language Pairs

Dogru,

Moorkens

2024

JoSTrans

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This study aims to investigate the effect of data augmentation through translation memories for desktop machine translation (MT) fine-tuning in OPUS-CAT. It also focuses on assessing the usefulness of desktop MT for professional translators. Engines in three language pairs (English → Turkish, English → Spanish, and English → Catalan) are fine-tuned with corpora of two different sizes. The translation quality of each engine is measured through automatic evaluation metrics (BLEU, chrF2, TER and COMET) and human evaluation metrics (ranking, adequacy and fluency). Overall evaluation results indicate promising quality improvements in all three language pairs and imply that the use of desktop MT applications such as OPUS-CAT and fine-tuning MT engines with custom data in a translator’s desktop can potentially provide high-quality translations aside from their advantages such as privacy, confidentiality and low use of computation power.

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Section: Methodology and Research Designmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Augmentation with Translation Memories for Desktop Machine Translation Fine-tuning in 3 Language Pairs

Dogru,

Moorkens

2024

JoSTrans

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“…OPUS-CAT (Nieminen, 2021) is a collection of open source software consisting of a local neural machine translation (NMT) engine and plugins for computer-assisted translation (CAT) tools, such as Trados, memoQ and OmegaT. OPUS-CAT enables the use of NMT models trained in the OPUS-MT project (Tiedemann and Thottingal, 2020) in professional translation.…”

Section: Introductionmentioning

confidence: 99%

OPUS-CAT Terminology Systems for the WMT23 Terminology Shared Task

Nieminen

2023

Proceedings of the Eighth Conference on Machine Translation

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This paper describes the submission of the OPUS-CAT project to the WMT 2023 terminology shared task. We trained systems for all three language pairs included in the task. All systems were trained using the same training pipeline with identical methods. Support for terminology was implemented by using the currently popular method of annotating source language terms in the training data with the corresponding target language terms.

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The boundaries of meaning: a case study in neural machine translation

Balashov

2022

Inquiry

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The success of deep learning in natural language processing raises intriguing questions about the nature of linguistic meaning and ways in which it can be processed by natural and artificial systems. One such question has to do with subword segmentation algorithms widely employed in language modeling, machine translation, and other tasks since 2016. These algorithms often cut words into semantically opaque pieces, such as 'period', 'on', 't', and 'ist' in 'period|on|t|ist'. The system then represents the resulting segments in a dense vector space, which is expected to model grammatical relations among them. This representation may in turn be used to map 'period|on|t|ist' (English) to 'par|od|ont|iste' (French). Thus, instead of being modeled at the lexical level, translation is reformulated more generally as the task of learning the best bilingual mapping between the sequences of subword segments of two languages; and sometimes even between pure character sequences: 'p|e|r|i|o|d|o|n|t|i|s|t|' → 'p|a|r|o|d|o|n|t|i|s|t|e'. Such subword segmentations and alignments are at work in highly efficient end-to-end machine translation systems, despite their allegedly opaque nature. The computational value of such processes is unquestionable. But do they have any linguistic or philosophical plausibility? I attempt to cast light on this question by reviewing the relevant details of the subword segmentation algorithms and by relating them to important philosophical and linguistic debates, in the spirit of making artificial intelligence more transparent and explainable.

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OPUS-CAT: Desktop NMT with CAT integration and local fine-tuning

Cited by 4 publications

References 9 publications

Data Augmentation with Translation Memories for Desktop Machine Translation Fine-tuning in 3 Language Pairs

Data Augmentation with Translation Memories for Desktop Machine Translation Fine-tuning in 3 Language Pairs

OPUS-CAT Terminology Systems for the WMT23 Terminology Shared Task

The boundaries of meaning: a case study in neural machine translation

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