Prediction of Translation Techniques for the Translation Process
- URL: http://arxiv.org/abs/2403.14454v1
- Date: Thu, 21 Mar 2024 15:02:03 GMT
- Title: Prediction of Translation Techniques for the Translation Process
- Authors: Fan Zhou, Vincent Vandeghinste,
- Abstract summary: The study differentiates between two scenarios of the translation process: from-scratch translation and post-editing.
The findings indicate that the predictive accuracy for from-scratch translation reaches 82%, while the post-editing process exhibits even greater potential, achieving an accuracy rate of 93%.
- Score: 6.30737834823321
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Machine translation (MT) encompasses a variety of methodologies aimed at enhancing the accuracy of translations. In contrast, the process of human-generated translation relies on a wide range of translation techniques, which are crucial for ensuring linguistic adequacy and fluency. This study suggests that these translation techniques could further optimize machine translation if they are automatically identified before being applied to guide the translation process effectively. The study differentiates between two scenarios of the translation process: from-scratch translation and post-editing. For each scenario, a specific set of experiments has been designed to forecast the most appropriate translation techniques. The findings indicate that the predictive accuracy for from-scratch translation reaches 82%, while the post-editing process exhibits even greater potential, achieving an accuracy rate of 93%.
Related papers
- Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts [43.68711076100652]
We propose a framework that engages language models in a multi-turn interaction, encompassing pre-translation research, drafting, refining, and proofreading.
We show that translating step-by-step yields large translation quality improvements over conventional zero-shot prompting approaches.
arXiv Detail & Related papers (2024-09-10T18:02:21Z) - BiVert: Bidirectional Vocabulary Evaluation using Relations for Machine
Translation [4.651581292181871]
We propose a bidirectional semantic-based evaluation method designed to assess the sense distance of the translation from the source text.
This approach employs the comprehensive multilingual encyclopedic dictionary BabelNet.
Factual analysis shows a strong correlation between the average evaluation scores generated by our method and the human assessments across various machine translation systems for English-German language pair.
arXiv Detail & Related papers (2024-03-06T08:02:21Z) - Machine Translation Models are Zero-Shot Detectors of Translation Direction [46.41883195574249]
Detecting the translation direction of parallel text has applications for machine translation training and evaluation, but also has forensic applications such as resolving plagiarism or forgery allegations.
In this work, we explore an unsupervised approach to translation direction detection based on the simple hypothesis that $p(texttranslation|textoriginal)>p(textoriginal|texttranslation)$, motivated by the well-known simplification effect in translationese or machine-translationese.
arXiv Detail & Related papers (2024-01-12T18:59:02Z) - Crossing the Threshold: Idiomatic Machine Translation through Retrieval
Augmentation and Loss Weighting [66.02718577386426]
We provide a simple characterization of idiomatic translation and related issues.
We conduct a synthetic experiment revealing a tipping point at which transformer-based machine translation models correctly default to idiomatic translations.
To improve translation of natural idioms, we introduce two straightforward yet effective techniques.
arXiv Detail & Related papers (2023-10-10T23:47:25Z) - The Best of Both Worlds: Combining Human and Machine Translations for
Multilingual Semantic Parsing with Active Learning [50.320178219081484]
We propose an active learning approach that exploits the strengths of both human and machine translations.
An ideal utterance selection can significantly reduce the error and bias in the translated data.
arXiv Detail & Related papers (2023-05-22T05:57:47Z) - Towards Debiasing Translation Artifacts [15.991970288297443]
We propose a novel approach to reducing translationese by extending an established bias-removal technique.
We use the Iterative Null-space Projection (INLP) algorithm, and show by measuring classification accuracy before and after debiasing, that translationese is reduced at both sentence and word level.
To the best of our knowledge, this is the first study to debias translationese as represented in latent embedding space.
arXiv Detail & Related papers (2022-05-16T21:46:51Z) - DEEP: DEnoising Entity Pre-training for Neural Machine Translation [123.6686940355937]
It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus.
We propose DEEP, a DEnoising Entity Pre-training method that leverages large amounts of monolingual data and a knowledge base to improve named entity translation accuracy within sentences.
arXiv Detail & Related papers (2021-11-14T17:28:09Z) - Modelling Latent Translations for Cross-Lingual Transfer [47.61502999819699]
We propose a new technique that integrates both steps of the traditional pipeline (translation and classification) into a single model.
We evaluate our novel latent translation-based model on a series of multilingual NLU tasks.
We report gains for both zero-shot and few-shot learning setups, up to 2.7 accuracy points on average.
arXiv Detail & Related papers (2021-07-23T17:11:27Z) - Translation Artifacts in Cross-lingual Transfer Learning [51.66536640084888]
We show that machine translation can introduce subtle artifacts that have a notable impact in existing cross-lingual models.
In natural language inference, translating the premise and the hypothesis independently can reduce the lexical overlap between them.
We also improve the state-of-the-art in XNLI for the translate-test and zero-shot approaches by 4.3 and 2.8 points, respectively.
arXiv Detail & Related papers (2020-04-09T17:54:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.