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Neural Machine Translation for Low-resource Languages: A Survey [PDF]
ACM Computing Surveys, 2021 Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since the early 2000s and has already entered a mature phase. While considered the most widely used solution for Machine Translation, its performance on low-resource ...
Surangika Ranathunga +5 more
semanticscholar +1 more source
Evaluation of English–Slovak Neural and Statistical Machine Translation
Applied Sciences, 2021 This study is focused on the comparison of phrase-based statistical machine translation (SMT) systems and neural machine translation (NMT) systems using automatic metrics for translation quality evaluation for the language pair of English and Slovak.
Lucia Benkova +3 more
doaj +1 more source
A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Non-autoregressive (NAR) generation, which is first proposed in neural machine translation (NMT) to speed up inference, has attracted much attention in both machine learning and natural language processing communities.
Yisheng Xiao +6 more
semanticscholar +1 more source
Translation Mechanism of Neural Machine Algorithm for Online English Resources
Complexity, 2021 At the level of English resource vocabulary, due to the lack of vocabulary alignment structure, the translation of neural machine translation has the problem of unfaithfulness.
Yanping Ye
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
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
Neural Machine Translation of Spanish-English Food Recipes Using LSTM
JOIV: International Journal on Informatics Visualization, 2022 Nowadays, food is one of the things that has been globalized, and everyone from different parts of the world has been able to cook food from other countries through existing online recipes.
Khen Dedes +5 more
doaj +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
Improving Neural Machine Translation Models with Monolingual Data [PDF]
Annual Meeting of the Association for Computational Linguistics, 2015 Neural Machine Translation (NMT) has obtained state-of-the art performance for several language pairs, while only using parallel data for training. Target-side monolingual data plays an important role in boosting fluency for phrase-based statistical ...
Rico Sennrich +2 more
semanticscholar +1 more source
Quality-Aware Decoding for Neural Machine Translation [PDF]
North American Chapter of the Association for Computational Linguistics, 2022 Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model (MAP
Patrick Fernandes +6 more
semanticscholar +1 more source