Results 61 to 70 of about 4,505,015 (366)
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
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
Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation [PDF]
Transactions of the Association for Computational Linguistics, 2016 We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token ...
Melvin Johnson +11 more
semanticscholar +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
OpenNMT: Open-Source Toolkit for Neural Machine Translation [PDF]
Annual Meeting of the Association for Computational Linguistics, 2017 We describe an open-source toolkit for neural machine translation (NMT). The toolkit prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research into model architectures, feature representations, and source modalities ...
Guillaume Klein +4 more
semanticscholar +1 more source
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
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
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
Identifying the machine translation error types with the greatest impact on post-editing effort [PDF]
, 2017 Translation Environment Tools make translators' work easier by providing them with term lists, translation memories and machine translation output. Ideally, such tools automatically predict whether it is more effortful to post-edit than to translate from
Daems, Joke +3 more
core +3 more sources