Results 21 to 30 of about 4,505,015 (366)
Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation [PDF]
Transactions of the Association for Computational Linguistics, 2021 Human evaluation of modern high-quality machine translation systems is a difficult problem, and there is increasing evidence that inadequate evaluation procedures can lead to erroneous conclusions.
Markus Freitag +5 more
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A Survey on Hybrid Machine Translation [PDF]
E3S Web of Conferences, 2020 Machine translation has gradually developed in past 1940’s.It has gained more and more attention because of effective and efficient nature. As it makes the translation automatically without the involvement of human efforts. The distinct models of machine
Anugu Anusha, Ramesh Gajula
doaj +1 more source
Neural Machine Translation of Rare Words with Subword Units [PDF]
Annual Meeting of the Association for Computational Linguistics, 2015 Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary.
Rico Sennrich +2 more
semanticscholar +1 more source
Multilingual Denoising Pre-training for Neural Machine Translation [PDF]
Transactions of the Association for Computational Linguistics, 2020 This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks.
Yinhan Liu +7 more
semanticscholar +1 more source
The Flores-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation [PDF]
Transactions of the Association for Computational Linguistics, 2021 One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource languages, consider only restricted ...
Naman Goyal +9 more
semanticscholar +1 more source
Effective Approaches to Attention-based Neural Machine Translation [PDF]
Conference on Empirical Methods in Natural Language Processing, 2015 An attentional mechanism has lately been used to improve neural machine translation (NMT) by selectively focusing on parts of the source sentence during translation.
Thang Luong +2 more
semanticscholar +1 more source
The unreasonable effectiveness of few-shot learning for machine translation [PDF]
International Conference on Machine Learning, 2023 We demonstrate the potential of few-shot translation systems, trained with unpaired language data, for both high and low-resource language pairs. We show that with only 5 examples of high-quality translation data shown at inference, a transformer decoder-
Xavier García +7 more
semanticscholar +1 more source
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation [PDF]
Conference on Empirical Methods in Natural Language Processing, 2014 In this paper, we propose a novel neural network model called RNN Encoder‐ Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the ...
Kyunghyun Cho +6 more
semanticscholar +1 more source
CURE: Code-Aware Neural Machine Translation for Automatic Program Repair [PDF]
International Conference on Software Engineering, 2021 Automatic program repair (APR) is crucial to improve software reliability. Recently, neural machine translation (NMT) techniques have been used to automatically fix software bugs. While promising, these approaches have two major limitations. Their search
Nan Jiang, Thibaud Lutellier, Lin Tan
semanticscholar +1 more source
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches [PDF]
SSST@EMNLP, 2014 Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder.
Kyunghyun Cho +3 more
semanticscholar +1 more source