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Register a new userMachine Translation Post-Editing (MTPE) from the Perspective of Translation Trainees: Implications for Translation Pedagogy
الترجمة الآلية بعد التحرير (MTPE) من منظور المتدربين الترجمة: الآثار المترتبة على علم الترميز
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تقدم هذه الورقة بيانات عن تصورات تدريب المتدربين في عملية MTPE وآثار التدريب على التدريب في هذا المجال.تهدف هذه الدراسة إلى تحليل أداء المتدربين في ثلاثة مهام MTPE الزوجية باللغة الإنجليزية البولندية ومقابلات ما بعد المهام لتحديد الحاجة إلى تعزيز مهارات تحرير الترجمة الآلية في تثقيف طلاب الترجمة.نظرا لأن القليل جدا من المعلومات المتعلقة بتدريب MTPE متاح، فقد يتم العثور على هذه الدراسة مفيدة.
This paper introduces data on translation trainees' perceptions of the MTPE process and implications on training in this field. This study aims to analyse trainees' performance of three MTPE tasks the English-Polish language pair and post-tasks interviews to determine the need to promote machine translation post-editing skills in educating translation students. Since very little information concerning MTPE training is available, this study may be found advantageous.
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The development of Translation Technologies, like Translation Memory and Machine Translation, has completely changed the translation industry and translator's workflow in the last decades. Nevertheless, TM and MT have been developed separately until
very recently. This ongoing project will study the external integration of TM and MT, examining if the productivity and post-editing efforts of translators are higher or lower than using only TM. To this end, we will conduct an experiment where Translation students and professional translators will be asked to translate two short texts; then we will check the post-editing efforts (temporal, technical and cognitive efforts) and the quality of the translated texts.
Automatic post-editing (APE) models are usedto correct machine translation (MT) system outputs by learning from human post-editing patterns. We present the system used in our submission to the WMT'21 Automatic Post-Editing (APE) English-German (En-De
) shared task. We leverage the state-of-the-art MT system (Ng et al., 2019) for this task. For further improvements, we adapt the MT model to the task domain by using WikiMatrix (Schwenket al., 2021) followed by fine-tuning with additional APE samples from previous editions of the shared task (WMT-16,17,18) and ensembling the models. Our systems beat the baseline on TER scores on the WMT'21 test set.
Zero-shot translation, directly translating between language pairs unseen in training, is a promising capability of multilingual neural machine translation (NMT). However, it usually suffers from capturing spurious correlations between the output lan
guage and language invariant semantics due to the maximum likelihood training objective, leading to poor transfer performance on zero-shot translation. In this paper, we introduce a denoising autoencoder objective based on pivot language into traditional training objective to improve the translation accuracy on zero-shot directions. The theoretical analysis from the perspective of latent variables shows that our approach actually implicitly maximizes the probability distributions for zero-shot directions. On two benchmark machine translation datasets, we demonstrate that the proposed method is able to effectively eliminate the spurious correlations and significantly outperforms state-of-the-art methods with a remarkable performance. Our code is available at https://github.com/Victorwz/zs-nmt-dae.
Accurate translation requires document-level information, which is ignored by sentence-level machine translation. Recent work has demonstrated that document-level consistency can be improved with automatic post-editing (APE) using only target-languag
e (TL) information. We study an extended APE model that additionally integrates source context. A human evaluation of fluency and adequacy in English--Russian translation reveals that the model with access to source context significantly outperforms monolingual APE in terms of adequacy, an effect largely ignored by automatic evaluation metrics. Our results show that TL-only modelling increases fluency without improving adequacy, demonstrating the need for conditioning on source text for automatic post-editing. They also highlight blind spots in automatic methods for targeted evaluation and demonstrate the need for human assessment to evaluate document-level translation quality reliably.
This paper describes our system (Team ID: nictrb) for participating in the WAT'21 restricted machine translation task. In our submitted system, we designed a new training approach for restricted machine translation. By sampling from the translation t
arget, we can solve the problem that ordinary training data does not have a restricted vocabulary. With the further help of constrained decoding in the inference phase, we achieved better results than the baseline, confirming the effectiveness of our solution. In addition, we also tried the vanilla and sparse Transformer as the backbone network of the model, as well as model ensembling, which further improved the final translation performance.
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