scholarly journals Research on Uyghur-Chinese Neural Machine Translation Based on the Transformer at Multistrategy Segmentation Granularity

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhiwang Xu ◽  
Huibin Qin ◽  
Yongzhu Hua
Keyword(s):  
Neural Networks ◽  
Machine Translation ◽  
Semantic Features ◽  
Training Method ◽  
Chinese Translation ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Parallel Corpus ◽  
Model Based ◽  
Translation Systems

In recent years, machine translation based on neural networks has become the mainstream method in the field of machine translation, but there are still challenges of insufficient parallel corpus and sparse data in the field of low resource translation. Existing machine translation models are usually trained on word-granularity segmentation datasets. However, different segmentation granularities contain different grammatical and semantic features and information. Only considering word granularity will restrict the efficient training of neural machine translation systems. Aiming at the problem of data sparseness caused by the lack of Uyghur-Chinese parallel corpus and complex Uyghur morphology, this paper proposes a multistrategy segmentation granular training method for syllables, marked syllable, words, and syllable word fusion and targets traditional recurrent neural networks and convolutional neural networks; the disadvantage of the network is to build a Transformer Uyghur-Chinese Neural Machine Translation model based entirely on the multihead self-attention mechanism. In CCMT2019, dimension results on Uyghur-Chinese bilingual datasets show that the effect of multiple translation granularity training method is significantly better than the rest of granularity segmentation translation systems, while the Transformer model can obtain higher BLEU value than Uyghur-Chinese translation model based on Self-Attention-RNN.

scholarly journals Investigating usability in postediting neural machine translation: Evidence from translation trainees' self-perception and performance

2021 ◽  
Vol 22 (1) ◽  
pp. 100-123
Author(s):  
Xiangling Wang ◽  
Tingting Wang ◽  
Ricardo Muñoz Martín ◽  
Yanfang Jia
Keyword(s):  
Machine Translation ◽  
Processing Speed ◽  
Text Processing ◽  
Ease Of Use ◽  
Chinese Translation ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Retrospective Reports ◽  
And Performance ◽  
Translation Systems

AbstractThis is a report on an empirical study on the usability for translation trainees of neural machine translation systems when post-editing (mtpe). Sixty Chinese translation trainees completed a questionnaire on their perceptions of mtpe's usability. Fifty of them later performed both a post-editing task and a regular translation task, designed to examine mtpe's usability by comparing their performance in terms of text processing speed, effort, and translation quality. Contrasting data collected by the questionnaire, keylogging, eyetracking and retrospective reports we found that, compared with regular, unaided translation, mtpe's usefulness in performance was remarkable: (1) it increased translation trainees' text processing speed and also improved their translation quality; (2) mtpe's ease of use in performance was partly proved in that it significantly reduced informants' effort as measured by (a) fixation duration and fixation counts; (b) total task time; and (c) the number of insertion keystrokes and total keystrokes. However, (3) translation trainees generally perceived mtpe to be useful to increase productivity, but they were skeptical about its use to improve quality. They were neutral towards the ease of use of mtpe.

scholarly journals Maximum Expected Likelihood Estimation for Zero-resource Neural Machine Translation

2017 ◽  
Author(s):  
Hao Zheng ◽  
Yong Cheng ◽  
Yang Liu
Keyword(s):  
Machine Translation ◽  
Likelihood Estimation ◽  
Neural Machine Translation ◽  
Parallel Corpora ◽  
Translation Model ◽  
Parallel Corpus ◽  
Target Model ◽  
High Resource ◽  
Remarkable Progress

While neural machine translation (NMT) has made remarkable progress in translating a handful of high-resource language pairs recently, parallel corpora are not always available for many zero-resource language pairs. To deal with this problem, we propose an approach to zero-resource NMT via maximum expected likelihood estimation. The basic idea is to maximize the expectation with respect to a pivot-to-source translation model for the intended source-to-target model on a pivot-target parallel corpus. To approximate the expectation, we propose two methods to connect the pivot-to-source and source-to-target models. Experiments on two zero-resource language pairs show that the proposed approach yields substantial gains over baseline methods. We also observe that when trained jointly with the source-to-target model, the pivot-to-source translation model also obtains improvements over independent training.

scholarly journals Source-Word Decomposition for Neural Machine Translation

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Thien Nguyen ◽  
Hoai Le ◽  
Van-Huy Pham
Keyword(s):  
Machine Translation ◽  
Linguistic Features ◽  
Specialized Knowledge ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Part Of Speech ◽  
Effective System ◽  
Feature Based ◽  
Transformer Model ◽  
Translation Systems

End-to-end neural machine translation does not require us to have specialized knowledge of investigated language pairs in building an effective system. On the other hand, feature engineering proves to be vital in other artificial intelligence fields, such as speech recognition and computer vision. Inspired by works in those fields, in this paper, we propose a novel feature-based translation model by modifying the state-of-the-art transformer model. Specifically, the encoder of the modified transformer model takes input combinations of linguistic features comprising of lemma, dependency label, part-of-speech tag, and morphological label instead of source words. The experiment results for the Russian-Vietnamese language pair show that the proposed feature-based transformer model improves over the strongest baseline transformer translation model by impressive 4.83 BLEU. In addition, experiment analysis reveals that human judgment on the translation results strongly confirms machine judgment. Our model could be useful in building translation systems translating from a highly inflectional language into a noninflectional language.

scholarly journals A Neural Machine Translation Model Based on Sequence to Dependency

2021 ◽  
Vol 2030 (1) ◽  
pp. 012062
Author(s):  
Ting Yang ◽  
Shinan Zhao ◽  
He Chen ◽  
Ting Liu
Keyword(s):  
Machine Translation ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Model Based

scholarly journals Pre-Training on Mixed Data for Low-Resource Neural Machine Translation

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 133
Author(s):  
Wenbo Zhang ◽  
Xiao Li ◽  
Yating Yang ◽  
Rui Dong
Keyword(s):  
Machine Translation ◽  
Training Model ◽  
Fine Tuning ◽  
Mixed Data ◽  
Training Method ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Training Models ◽  
Low Resource ◽  
Word Translation

The pre-training fine-tuning mode has been shown to be effective for low resource neural machine translation. In this mode, pre-training models trained on monolingual data are used to initiate translation models to transfer knowledge from monolingual data into translation models. In recent years, pre-training models usually take sentences with randomly masked words as input, and are trained by predicting these masked words based on unmasked words. In this paper, we propose a new pre-training method that still predicts masked words, but randomly replaces some of the unmasked words in the input with their translation words in another language. The translation words are from bilingual data, so that the data for pre-training contains both monolingual data and bilingual data. We conduct experiments on Uyghur-Chinese corpus to evaluate our method. The experimental results show that our method can make the pre-training model have a better generalization ability and help the translation model to achieve better performance. Through a word translation task, we also demonstrate that our method enables the embedding of the translation model to acquire more alignment knowledge.

scholarly journals Keeping Models Consistent between Pretraining and Translation for Low-Resource Neural Machine Translation

2020 ◽  
Vol 12 (12) ◽  
pp. 215
Author(s):  
Wenbo Zhang ◽  
Xiao Li ◽  
Yating Yang ◽  
Rui Dong ◽  
Gongxu Luo
Keyword(s):  
Machine Translation ◽  
Language Model ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Parallel Corpus ◽  
Model Experiments ◽  
Low Resource ◽  
Translation Quality ◽  
Number Of Layers ◽  
Cross Lingual

Recently, the pretraining of models has been successfully applied to unsupervised and semi-supervised neural machine translation. A cross-lingual language model uses a pretrained masked language model to initialize the encoder and decoder of the translation model, which greatly improves the translation quality. However, because of a mismatch in the number of layers, the pretrained model can only initialize part of the decoder’s parameters. In this paper, we use a layer-wise coordination transformer and a consistent pretraining translation transformer instead of a vanilla transformer as the translation model. The former has only an encoder, and the latter has an encoder and a decoder, but the encoder and decoder have exactly the same parameters. Both models can guarantee that all parameters in the translation model can be initialized by the pretrained model. Experiments on the Chinese–English and English–German datasets show that compared with the vanilla transformer baseline, our models achieve better performance with fewer parameters when the parallel corpus is small.

Analyzing Subword Techniques to Improve English to Sinhala Neural Machine Translation

2021 ◽  
pp. 2050017
Author(s):  
Rashmini Naranpanawa ◽  
Ravinga Perera ◽  
Thilakshi Fonseka ◽  
Uthayasanker Thayasivam
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Rare Word ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
Low Resource ◽  
Word Level ◽  
Morphologically Rich Languages

Neural machine translation (NMT) is a remarkable approach which performs much better than the Statistical machine translation (SMT) models when there is an abundance of parallel corpus. However, vanilla NMT is primarily based upon word-level with a fixed vocabulary. Therefore, low resource morphologically rich languages such as Sinhala are mostly affected by the out of vocabulary (OOV) and Rare word problems. Recent advancements in subword techniques have opened up opportunities for low resource communities by enabling open vocabulary translation. In this paper, we extend our recently published state-of-the-art EN-SI translation system using the transformer and explore standard subword techniques on top of it to identify which subword approach has a greater effect on English Sinhala language pair. Our models demonstrate that subword segmentation strategies along with the state-of-the-art NMT can perform remarkably when translating English sentences into a rich morphology language regardless of a large parallel corpus.

scholarly journals Is automation changing the translation profession?

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Anthony Pym ◽  
Ester Torres-Simón
Keyword(s):  
Machine Translation ◽  
Communication Skills ◽  
Service Providers ◽  
Time Data ◽  
Market Segments ◽  
Interactive Communication ◽  
Neural Machine Translation ◽  
History Of ◽  
Translation Services ◽  
Translation Systems

Abstract As a language-intensive profession, translation is of frontline interest in the era of language automation. In particular, the development of neural machine translation systems since 2016 has brought with it fears that soon there will be no more human translators. When considered in terms of the history of automation, however, any such direct effect is far from obvious: the translation industry is still growing and machine translation is only one instance of automation. At the same time, data on remuneration indicate structural wage dispersion in professional translation services, with some signs that this dispersion may increase in certain market segments as automated workflows and translation technologies are adopted more by large language-service providers more than by smaller companies and individual freelancers. An analysis of recent changes in discourses on and in the translation profession further indicates conceptual adjustments in the profession that may be attributed to growing automation, particularly with respect to expanding skills set associated with translation, the tendency to combine translation with other forms of communication, and the use of interactive communication skills to authorize and humanize the results of automation.

Sign in / Sign up

Export Citation Format

Share Document