scholarly journals A Study of Neural Machine Translation from Chinese to Urdu

2020 ◽  
Vol 2 (4) ◽  
pp. 28
Author(s):  
. Zeeshan
Keyword(s):  
Machine Translation ◽  
Chinese Language ◽  
Language Translation ◽  
Target Language ◽  
Foreign Languages ◽  
Neural Machine Translation ◽  
Source Language ◽  
Great Progress ◽  
Score Method ◽  
Translation Methods

Machine Translation (MT) is used for giving a translation from a source language to a target language. Machine translation simply translates text or speech from one language to another language, but this process is not sufficient to give the perfect translation of a text due to the requirement of identification of whole expressions and their direct counterparts. Neural Machine Translation (NMT) is one of the most standard machine translation methods, which has made great progress in the recent years especially in non-universal languages. However, local language translation software for other foreign languages is limited and needs improving. In this paper, the Chinese language is translated to the Urdu language with the help of Open Neural Machine Translation (OpenNMT) in Deep Learning. Firstly, a Chineseto Urdu language sentences datasets were established and supported with Seven million sentences. After that, these datasets were trained by using the Open Neural Machine Translation (OpenNMT) method. At the final stage, the translation was compared to the desired translation with the help of the Bleu Score Method.

scholarly journals Knowledge Graphs Effectiveness in Neural Machine Translation Improvement

2020 ◽  
Vol 21 (3) ◽  
Author(s):  
Benyamin Ahmadnia ◽  
Bonnie J. Dorr ◽  
Parisa Kordjamshidi
Keyword(s):  
Machine Translation ◽  
Semantic Representation ◽  
Language Translation ◽  
Semantic Relations ◽  
Training Data ◽  
Target Language ◽  
Neural Machine Translation ◽  
Source Language ◽  
Knowledge Graphs ◽  
Unknown Words

Neural Machine Translation (NMT) systems require a massive amount of Maintaining semantic relations between words during the translation process yields more accurate target-language output from Neural Machine Translation (NMT). Although difficult to achieve from training data alone, it is possible to leverage Knowledge Graphs (KGs) to retain source-language semantic relations in the corresponding target-language translation. The core idea is to use KG entity relations as embedding constraints to improve the mapping from source to target. This paper describes two embedding constraints, both of which employ Entity Linking (EL)---assigning a unique identity to entities---to associate words in training sentences with those in the KG: (1) a monolingual embedding constraint that supports an enhanced semantic representation of the source words through access to relations between entities in a KG; and (2) a bilingual embedding constraint that forces entity relations in the source-language to be carried over to the corresponding entities in the target-language translation. The method is evaluated for English-Spanish translation exploiting Freebase as a source of knowledge. Our experimental results show that exploiting KG information not only decreases the number of unknown words in the translation but also improves translation quality.

scholarly journals Controlling Neural Machine Translation Formality with Synthetic Supervision

2020 ◽  
Vol 34 (05) ◽  
pp. 8568-8575
Author(s):  
Xing Niu ◽  
Marine Carpuat
Keyword(s):  
Machine Translation ◽  
Target Language ◽  
Sentence Pair ◽  
English Sentence ◽  
Neural Machine Translation ◽  
Source Language ◽  
Training Scheme ◽  
Training Examples ◽  
Language Content ◽  
Missing Element

This work aims to produce translations that convey source language content at a formality level that is appropriate for a particular audience. Framing this problem as a neural sequence-to-sequence task ideally requires training triplets consisting of a bilingual sentence pair labeled with target language formality. However, in practice, available training examples are limited to English sentence pairs of different styles, and bilingual parallel sentences of unknown formality. We introduce a novel training scheme for multi-task models that automatically generates synthetic training triplets by inferring the missing element on the fly, thus enabling end-to-end training. Comprehensive automatic and human assessments show that our best model outperforms existing models by producing translations that better match desired formality levels while preserving the source meaning.1

Translation of Sentence Lampung-Indonesian Languages with Neural Machine Translation Attention Based Approach

2018 ◽  
Vol 6 (02) ◽  
pp. 191-206
Author(s):  
Zaenal Abidin
Keyword(s):  
Machine Translation ◽  
Language Translation ◽  
Training Process ◽  
Neural Machine Translation ◽  
New Approach ◽  
Source Language ◽  
Average Value ◽  
Approach Method ◽  
Stable Vectors ◽  
Network Component

In this research, automatically Lampung language translation into the Indonesian language was using neural machine translation (NMT) attention based approach. NMT, a new approach method in machine translation technology, that has worked by combining the encoder and decoder. The encoder in NMT is a recurrent neural network component that encrypts the source language to several length-stable vectors and the decoder is a recurrent neural networks component that generates translation result comprehensive. NMT Research has begun with creating a pair of 3000 parallel sentences of Lampung language (api dialect) and Indonesian language. Then it continues to decide the NMT parameter model for the data training process. The next step is building NMT model and evaluate it. The testing of this approach has used 25 single sentences without out-of-vocabulary (OOV), 25 single sentences with OOV, 25 plural sentences without OOV, and 25 plural sentences with OOV. The testing translation result using NMT attention shows the bilingual evaluation understudy (BLEU) an average value is 51, 96 %.

scholarly journals TRANSLATION OF ENGLISH TASKS INTO INDONESIAN THROUGH ONLINE MACHINE TRANSLATION PROGRAM

2017 ◽  
Vol 4 (1) ◽  
pp. 103
Author(s):  
Emzir Emzir ◽  
Ninuk Lustyantie ◽  
Akbar Akbar
Keyword(s):  
Machine Translation ◽  
Language Translation ◽  
Doctoral Program ◽  
Deep Understanding ◽  
Language Education ◽  
Target Language ◽  
State University ◽  
Optimum Result ◽  
Source Language ◽  
Different Cultures

The objective of this research is to obtain a deep understanding about the online machine translation of graduate students in the Language Education Doctoral Program of State University of Jakarta, Indonesia, from source language to target language in order to achieve equivalence in the subject of Language Translation and Education. The approach used is qualitative approach with ethnography method. The translation process is conducted by writing down words or copying-pasting sentences to be translated and then those words/sentences will be automatically translated by machine translation. A repetitive edit, revision and correction process shall be first performed in order to get an optimum result i.e. translated sentences are equal in textual and meanings. The deviations occur due to inaccurate equivalents caused by different cultures between the source language and target language as well as the scope of translated language scientific field. The used strategy is a literal translation. Based on the research results, the translation of English tasks to Indonesian through the online translation program is very useful to facilitate the students’ lecturing process in completing their tasks.

scholarly journals Language To Language Translation System

2020 ◽  
pp. 289-293
Author(s):  
Ms Pratheeksha ◽  
Pratheeksha Rai ◽  
Ms Vijetha
Keyword(s):  
Speech Recognition ◽  
Machine Translation ◽  
Automatic Speech Recognition ◽  
Speech Synthesis ◽  
Language Translation ◽  
Target Language ◽  
Translation System ◽  
Text To Speech ◽  
Source Language ◽  
Text To Speech Synthesis

The system used in Language to Language Translation is the phrases spoken in one language are immediately spoken in other language by the device. Language to Language Translation is a three steps software process which includes Automatic Speech Recognition, Machine Translation and Voice Synthesis. Language to Language system includes the major speech translation projects using different approaches for Speech Recognition, Translation and Text to Speech synthesis highlighting the major pros and cons for the approach being used. Language translation is a process that takes the conversational phrase in one language as an input and translated speech phrases in another language as the output. The three components of language-to-language translation are connected in a sequential order. Automatic Speech Recognition (ASR) is responsible for converting the spoken phrases of source language to the text in the same language followed by machine translation which translates the source language to next target language text and finally the speech synthesizer is responsible for text to speech conversion of target language.

scholarly journals Unraveling the Contribution of Image Captioning and Neural Machine Translation for Multimodal Machine Translation

2017 ◽  
Vol 108 (1) ◽  
pp. 197-208 ◽  
Author(s):  
Chiraag Lala ◽  
Pranava Madhyastha ◽  
Josiah Wang ◽  
Lucia Specia
Keyword(s):  
Recent Work ◽  
Machine Translation ◽  
Visual Information ◽  
Target Language ◽  
Image Captioning ◽  
Neural Machine Translation ◽  
Source Language ◽  
Translation Quality ◽  
Depth Study ◽  
Image Descriptions

AbstractRecent work on multimodal machine translation has attempted to address the problem of producing target language image descriptions based on both the source language description and the corresponding image. However, existing work has not been conclusive on the contribution of visual information. This paper presents an in-depth study of the problem by examining the differences and complementarities of two related but distinct approaches to this task: textonly neural machine translation and image captioning. We analyse the scope for improvement and the effect of different data and settings to build models for these tasks. We also propose ways of combining these two approaches for improved translation quality.

Improved neural machine translation for low-resource English–Assamese pair

2021 ◽  
pp. 1-12
Author(s):  
Sahinur Rahman Laskar ◽  
Abdullah Faiz Ur Rahman Khilji ◽  
Partha Pakray ◽  
Sivaji Bandyopadhyay
Keyword(s):  
Machine Translation ◽  
Data Augmentation ◽  
Language Translation ◽  
Linguistically Diverse ◽  
Neural Machine Translation ◽  
Low Resource ◽  
Parallel Data ◽  
The World ◽  
Translation Accuracy ◽  
Vocabulary Problems

Language translation is essential to bring the world closer and plays a significant part in building a community among people of different linguistic backgrounds. Machine translation dramatically helps in removing the language barrier and allows easier communication among linguistically diverse communities. Due to the unavailability of resources, major languages of the world are accounted as low-resource languages. This leads to a challenging task of automating translation among various such languages to benefit indigenous speakers. This article investigates neural machine translation for the English–Assamese resource-poor language pair by tackling insufficient data and out-of-vocabulary problems. We have also proposed an approach of data augmentation-based NMT, which exploits synthetic parallel data and shows significantly improved translation accuracy for English-to-Assamese and Assamese-to-English translation and obtained state-of-the-art results.

scholarly journals Optimizing Tokenization Choice for Machine Translation across Multiple Target Languages

2017 ◽  
Vol 108 (1) ◽  
pp. 257-269 ◽  
Author(s):  
Nasser Zalmout ◽  
Nizar Habash
Keyword(s):  
Machine Translation ◽  
Performance Enhancement ◽  
Statistical Machine Translation ◽  
Target Language ◽  
Source Language ◽  
Context Variable ◽  
Significant Performance ◽  
Morphologically Rich Languages ◽  
Target Languages ◽  
Language Text

AbstractTokenization is very helpful for Statistical Machine Translation (SMT), especially when translating from morphologically rich languages. Typically, a single tokenization scheme is applied to the entire source-language text and regardless of the target language. In this paper, we evaluate the hypothesis that SMT performance may benefit from different tokenization schemes for different words within the same text, and also for different target languages. We apply this approach to Arabic as a source language, with five target languages of varying morphological complexity: English, French, Spanish, Russian and Chinese. Our results show that different target languages indeed require different source-language schemes; and a context-variable tokenization scheme can outperform a context-constant scheme with a statistically significant performance enhancement of about 1.4 BLEU points.

scholarly journals IntroVNMT: An Introspective Model for Variational Neural Machine Translation

2020 ◽  
Vol 34 (05) ◽  
pp. 8830-8837
Author(s):  
Xin Sheng ◽  
Linli Xu ◽  
Junliang Guo ◽  
Jingchang Liu ◽  
Ruoyu Zhao ◽  
...  
Keyword(s):  
Machine Translation ◽  
Latent Variables ◽  
Image Synthesis ◽  
Target Language ◽  
Generative Adversarial Network ◽  
Neural Machine Translation ◽  
Adversarial Network ◽  
Proposed Model ◽  
Model Training ◽  
High Level

We propose a novel introspective model for variational neural machine translation (IntroVNMT) in this paper, inspired by the recent successful application of introspective variational autoencoder (IntroVAE) in high quality image synthesis. Different from the vanilla variational NMT model, IntroVNMT is capable of improving itself introspectively by evaluating the quality of the generated target sentences according to the high-level latent variables of the real and generated target sentences. As a consequence of introspective training, the proposed model is able to discriminate between the generated and real sentences of the target language via the latent variables generated by the encoder of the model. In this way, IntroVNMT is able to generate more realistic target sentences in practice. In the meantime, IntroVNMT inherits the advantages of the variational autoencoders (VAEs), and the model training process is more stable than the generative adversarial network (GAN) based models. Experimental results on different translation tasks demonstrate that the proposed model can achieve significant improvements over the vanilla variational NMT model.

scholarly journals Identifying Semantics in Clinical Reports Using Neural Machine Translation

2019 ◽  
Vol 33 ◽  
pp. 9552-9557
Author(s):  
Srikanth Mujjiga ◽  
Vamsi Krishna ◽  
Kalyan Chakravarthi ◽  
Vijayananda J
Keyword(s):  
Machine Translation ◽  
Large Scale ◽  
Language Translation ◽  
Semantic Search ◽  
Neural Machine Translation ◽  
Healthcare Facilities ◽  
Semantic Level ◽  
Concept Space ◽  
Unique Approach ◽  
Retrieval Problem

Clinical documents are vital resources for radiologists when they have to consult or refer while studying similar cases. In large healthcare facilities where millions of reports are generated, searching for relevant documents is quite challenging. With abundant interchangeable words in clinical domain, understanding the semantics of the words in the clinical documents is vital to improve the search results. This paper details an end to end semantic search application to address the large scale information retrieval problem of clinical reports. The paper specifically focuses on the challenge of identifying semantics in the clinical reports to facilitate search at semantic level. The semantic search works by mapping the documents into the concept space and the search is performed in the concept space. A unique approach of framing the concept mapping problem as a language translation problem is proposed in this paper. The concept mapper is modelled using the Neural machine translation model (NMT) based on encoder-decoder with attention architecture. The regular expression based concept mapper takes approximately 3 seconds to extract UMLS concepts from a single document, where as the trained NMT does the same in approximately 30 milliseconds. NMT based model further enables incorporation of negation detection to identify whether a concept is negated or not, facilitating search for negated queries.

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