Results 31 to 40 of about 418,591 (309)
Interactive Machine Translation [PDF]
, 2011 [EN] Achieving high-quality translation between any pair of languages is not possible with the current Machine-Translation (MT) technology a human post-editing of the outputs of the MT system being necessary. Therefore, MT is a suitable area to apply the Interactive Pattern Recognition (IPR) framework and this application has led to what nowadays is ...
Toselli, Alejandro Héctor +5 more
openaire +2 more sources
Comparative Legilinguistics, 2019 With the advent of the neural paradigm, machine translation has made another leap in quality. As a result, its use by trainee translators has increased considerably, which cannot be disregarded in translation pedagogy.
Wiesmann Eva
doaj +4 more sources
Neural Machine Translation Advised by Statistical Machine Translation
Proceedings of the AAAI Conference on Artificial Intelligence, 2017 Neural Machine Translation (NMT) is a new approach to machine translation that has made great progress in recent years. However, recent studies show that NMT generally produces fluent but inadequate translations (Tu et al. 2016b; 2016a; He et al. 2016; Tu et al. 2017). This is in contrast to conventional Statistical Machine Translation (
Wang, Xing +5 more
openaire +2 more sources
Challenges in translational machine learning [PDF]
Human Genetics, 2022 AbstractMachine learning (ML) algorithms are increasingly being used to help implement clinical decision support systems. In this new field, we define as “translational machine learning”, joint efforts and strong communication between data scientists and clinicians help to span the gap between ML and its adoption in the clinic.
Artuur Couckuyt +6 more
openaire +3 more sources
Study on Post-editing for Machine Translation of Railway Engineering Texts [PDF]
SHS Web of Conferences, 2021 With rapid development of China's railways, there are more overseas construction projects and technical exchanges in the field of railway engineering, which have generated widespread demands for translation.
Li Yuting, Lu Xiuying
doaj +1 more source
Automatic Classification of Human Translation and Machine Translation: A Study from the Perspective of Lexical Diversity [PDF]
arXiv, 2021 By using a trigram model and fine-tuning a pretrained BERT model for sequence classification, we show that machine translation and human translation can be classified with an accuracy above chance level, which suggests that machine translation and human translation are different in a systematic way. The classification accuracy of machine translation is
arxiv
PETCI: A Parallel English Translation Dataset of Chinese Idioms [PDF]
arXiv, 2022 Idioms are an important language phenomenon in Chinese, but idiom translation is notoriously hard. Current machine translation models perform poorly on idiom translation, while idioms are sparse in many translation datasets. We present PETCI, a parallel English translation dataset of Chinese idioms, aiming to improve idiom translation by both human and
arxiv
Neural-based machine translation for medical text domain. Based on European Medicines Agency leaflet texts [PDF]
Procedia Computer Science, 2015, 64: 2-9, 2015 The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translation. It aims at building a jointly-
arxiv +1 more source
Cogent Arts & Humanities, 2023 This study aims to examine machine translation research in journals indexed in the Web of Science to find out the research trending issue, hotspot areas of research, and document co-citation analysis. To this end, 541 documents published between 1992 and
Mohammed Ali Mohsen +2 more
doaj +1 more source
Incorporating Human Translator Style into English-Turkish Literary Machine Translation [PDF]
24th Annual Conference of the European Association of Machine
Translation (EAMT), June 2023, Tampere, Finland, 2023 Although machine translation systems are mostly designed to serve in the general domain, there is a growing tendency to adapt these systems to other domains like literary translation. In this paper, we focus on English-Turkish literary translation and develop machine translation models that take into account the stylistic features of translators.
arxiv