Results 91 to 100 of about 491,933 (328)
Prompt-Driven Neural Machine Translation
Findings, 2022 Neural machine translation (NMT) has obtained significant performance improvement over the recent years. However, NMT models still face various challenges including fragility and lack of style flexibility.
Yafu Li +3 more
semanticscholar +1 more source
Neural Machine Translation with Reconstruction
Proceedings of the AAAI Conference on Artificial Intelligence, 2017 Although end-to-end Neural Machine Translation (NMT) has achieved remarkable progress in the past two years, it suffers from a major drawback: translations generated by NMT systems often lack of adequacy. It has been widely observed that NMT tends to repeatedly translate some source words while mistakenly ignoring other words.
Tu, Zhaopeng +4 more
openaire +2 more sources
Studia Rossica Posnaniensia, 2019 Machine translation (MT) is a relatively new field of science. MT systems are evolving in certain directions. The article discusses the possibilities and the future of systems currently offered to public by the biggest technological companies focusing on
Jakub Olas
doaj +1 more source
Applied Sciences, 2023 Neural machine translation has achieved good translation results, but needs further improvement in low-resource and domain-specific translation. To this end, the paper proposed to incorporate source language syntactic information into neural machine ...
Yanna Sang, Yuan Chen, Juwei Zhang
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Pre-Translation for Neural Machine Translation
, 2016 Recently, the development of neural machine translation (NMT) has significantly improved the translation quality of automatic machine translation. While most sentences are more accurate and fluent than translations by statistical machine translation (SMT)-based systems, in some cases, the NMT system produces translations that have a completely ...
Niehues, Jan +3 more
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Neural System Combination for Machine Translation
, 2017 Neural machine translation (NMT) becomes a new approach to machine translation and generates much more fluent results compared to statistical machine translation (SMT). However, SMT is usually better than NMT in translation adequacy.
Hu, Wenpeng +3 more
core +1 more source
Neural Network Machine Translation Method Based on Unsupervised Domain Adaptation
Complexity, 2020 Relying on large-scale parallel corpora, neural machine translation has achieved great success in certain language pairs. However, the acquisition of high-quality parallel corpus is one of the main difficulties in machine translation research.
Rui Wang
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Ancient Korean Neural Machine Translation
IEEE Access, 2020 Translation of the languages of ancient times can serve as a source for the content of various digital media and can be helpful in various fields such as natural phenomena, medicine, and science.
Chanjun Park +3 more
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A Stochastic Decoder for Neural Machine Translation [PDF]
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018 The process of translation is ambiguous, in that there are typically many valid trans- lations for a given sentence. This gives rise to significant variation in parallel cor- pora, however, most current models of machine translation do not account for this variation, instead treating the prob- lem as a deterministic process.
Schulz, P., Aziz, W., Cohn, T.
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Learning to Parse and Translate Improves Neural Machine Translation
, 2017 There has been relatively little attention to incorporating linguistic prior to neural machine translation. Much of the previous work was further constrained to considering linguistic prior on the source side.
Cho, Kyunghyun +2 more
core +1 more source