Results 1 to 10 of about 491,933 (328)
Neural Machine Translation [PDF]
Revista Tradumàtica, 2017 From the outset, automatic translation was dominated by systems based on linguistic information, but then later other approaches opened up the way, such as translation memories and statistical machine translation which draw on parallel language corpora ...
Francisco Casacuberta Nolla +1 more
doaj +4 more sources
Memory-augmented Neural Machine Translation [PDF]
Proceedings of the 2017 Conference on Empirical Methods in Natural
Language Processing, 2017 Neural machine translation (NMT) has achieved notable success in recent times, however it is also widely recognized that this approach has limitations with handling infrequent words and word pairs. This paper presents a novel memory-augmented NMT (M-NMT) architecture, which stores knowledge about how words (usually infrequently encountered ones) should
Yang Feng +4 more
core +8 more sources
The neural machine translation models for the low-resource Kazakh–English language pair [PDF]
PeerJ Computer Science, 2023 The development of the machine translation field was driven by people’s need to communicate with each other globally by automatically translating words, sentences, and texts from one language into another.
Vladislav Karyukin +4 more
doaj +3 more sources
Translating Phrases in Neural Machine Translation [PDF]
Proceedings of the 2017 Conference on Empirical Methods in Natural
Language Processing, 2017 Accepted by EMNLP ...
Xing Wang +3 more
openalex +4 more sources
Retrosynthetic reaction pathway prediction through neural machine translation of atomic environments. [PDF]
Nat Commun, 2022 Designing efficient synthetic routes for a target molecule remains a major challenge in organic synthesis. Atom environments are ideal, stand-alone, chemically meaningful building blocks providing a high-resolution molecular representation.
Ucak UV, Ashyrmamatov I, Ko J, Lee J.
europepmc +2 more sources
Scaling neural machine translation to 200 languages. [PDF]
NatureThe development of neural techniques has opened up new avenues for research in machine translation. Today, neural machine translation (NMT) systems can leverage highly multilingual capacities and even perform zero-shot translation, delivering promising ...
NLLB Team.
europepmc +2 more sources
Translating Akkadian to English with neural machine translation. [PDF]
PNAS Nexus, 2023 Abstract Cuneiform is one of the earliest writing systems in recorded human history (ca. 3,400 BCE–75 CE). Hundreds of thousands of such texts were found over the last two centuries, most of which are written in Sumerian and Akkadian.
Gutherz G +4 more
europepmc +3 more sources
Modeling Coverage for Neural Machine Translation [PDF]
Annual Meeting of the Association for Computational Linguistics, 2016 Attention mechanism has enhanced state-of-the-art Neural Machine Translation (NMT) by jointly learning to align and translate. It tends to ignore past alignment information, however, which often leads to over-translation and under-translation. To address
Zhaopeng Tu +4 more
openalex +3 more sources
Scaling Neural Machine Translation [PDF]
Proceedings of the Third Conference on Machine Translation: Research Papers, 2018 Sequence to sequence learning models still require several days to reach state of the art performance on large benchmark datasets using a single machine.
Myle Ott +3 more
semanticscholar +4 more sources
Sublemma-Based Neural Machine Translation [PDF]
Complexity, 2021 Powerful deep learning approach frees us from feature engineering in many artificial intelligence tasks. The approach is able to extract efficient representations from the input data, if the data are large enough. Unfortunately, it is not always possible
Thien Nguyen, Huu Nguyen, Phuoc Tran
doaj +2 more sources