Application of Machine-aided Translation System based on Multilingual Parallel Corpus in Japanese Translation of Traditional Culture

2021 ◽  
Author(s):  
Shan Li
Keyword(s):  
Traditional Culture ◽  
Translation System ◽  
Parallel Corpus ◽  
Japanese Translation

English-Dogri Translation System using MOSES

2016 ◽  
Vol 1 (1) ◽  
pp. 45-49
Author(s):  
Avinash Singh ◽  
Asmeet Kour ◽  
Shubhnandan S. Jamwal
Keyword(s):  
Natural Language Processing ◽  
Machine Translation ◽  
Language Processing ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Parallel Corpus ◽  
English System ◽  
Machine Translation System ◽  
Translation Machine ◽  
Language Pair

The objective behind this paper is to analyze the English-Dogri parallel corpus translation. Machine translation is the translation from one language into another language. Machine translation is the biggest application of the Natural Language Processing (NLP). Moses is statistical machine translation system allow to train translation models for any language pair. We have developed translation system using Statistical based approach which helps in translating English to Dogri and vice versa. The parallel corpus consists of 98,973 sentences. The system gives accuracy of 80% in translating English to Dogri and the system gives accuracy of 87% in translating Dogri to English system.

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 Improving Machine Translation Performance by Exploiting Non-Parallel Corpora

2005 ◽  
Vol 31 (4) ◽  
pp. 477-504 ◽  
Author(s):  
Dragos Stefan Munteanu ◽  
Daniel Marcu
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Parallel Corpora ◽  
Parallel Corpus ◽  
Scarce Resources ◽  
Parallel Data ◽  
Machine Translation System ◽  
Novel Method ◽  
Arabic And English

We present a novel method for discovering parallel sentences in comparable, non-parallel corpora. We train a maximum entropy classifier that, given a pair of sentences, can reliably determine whether or not they are translations of each other. Using this approach, we extract parallel data from large Chinese, Arabic, and English non-parallel newspaper corpora. We evaluate the quality of the extracted data by showing that it improves the performance of a state-of-the-art statistical machine translation system. We also show that a good-quality MT system can be built from scratch by starting with a very small parallel corpus (100,000 words) and exploiting a large non-parallel corpus. Thus, our method can be applied with great benefit to language pairs for which only scarce resources are available.

scholarly journals Machine Translation System Using Deep Learning for English to Urdu

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Syed Abdul Basit Andrabi ◽  
Abdul Wahid
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Communication Technology ◽  
Target Language ◽  
Translation System ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
Proposed Model ◽  
Learning Technique ◽  
Machine Translation System

Machine translation is an ongoing field of research from the last decades. The main aim of machine translation is to remove the language barrier. Earlier research in this field started with the direct word-to-word replacement of source language by the target language. Later on, with the advancement in computer and communication technology, there was a paradigm shift to data-driven models like statistical and neural machine translation approaches. In this paper, we have used a neural network-based deep learning technique for English to Urdu languages. Parallel corpus sizes of around 30923 sentences are used. The corpus contains sentences from English-Urdu parallel corpus, news, and sentences which are frequently used in day-to-day life. The corpus contains 542810 English tokens and 540924 Urdu tokens, and the proposed system is trained and tested using 70 : 30 criteria. In order to evaluate the efficiency of the proposed system, several automatic evaluation metrics are used, and the model output is also compared with the output from Google Translator. The proposed model has an average BLEU score of 45.83.

Building a parallel corpus of German/Swiss German Sign Language train announcements

2016 ◽  
Vol 21 (1) ◽  
pp. 116-129 ◽  
Author(s):  
Sarah Ebling
Keyword(s):  
Sign Language ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Notation System ◽  
Parallel Corpus ◽  
Swiss German ◽  
Video Recordings ◽  
Machine Translation System ◽  
German Sign Language ◽  
Animation System

We present a parallel corpus of German/Swiss German Sign Language train announcements. The corpus is used in a statistical machine translation system that translates from German to Swiss German Sign Language. The output of the translation system is then passed on to an animation system, the result being a sign language avatar representation on a mobile phone. Building the parallel corpus consisted of four steps: translating the written German train announcements into Swiss German Sign Language glosses, signing the announcements in front of a camera on the basis of the gloss transcriptions, notating the signs in the video recordings in a form-based sign language notation system, and adding information about non-manual features. The resulting corpus contains 3,241 sentence pairs, which makes it a large parallel corpus involving sign language.

Japanese translation teaching corpus based on bilingual non parallel data model

2020 ◽  
pp. 1-11
Author(s):  
Zheng Guo ◽  
Zhu Jifeng
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Data Model ◽  
Graph Representation ◽  
Small Scale ◽  
Translation Model ◽  
Parallel Corpus ◽  
Parallel Data ◽  
Intelligent Technology ◽  
Japanese Translation

In recent years, with the development of Internet and intelligent technology, Japanese translation teaching has gradually explored a new teaching mode. Under the guidance of natural language processing and intelligent machine translation, machine translation based on statistical model has gradually become one of the primary auxiliary tools in Japanese translation teaching. In order to solve the problems of small scale, slow speed and incomplete field in the traditional parallel corpus machine translation, this paper constructs a Japanese translation teaching corpus based on the bilingual non parallel data model, and uses this corpus to train Japanese translation teaching machine translation model Moses to get better auxiliary effect. In the process of construction, for non parallel corpus, we use the translation retrieval framework based on word graph representation to extract parallel sentence pairs from the corpus, and then build a translation retrieval model based on Bilingual non parallel data. The experimental results of training Moses translation model with Japanese translation corpus show that the bilingual nonparallel data model constructed in this paper has good translation retrieval performance. Compared with the existing algorithm, the Bleu value extracted in the parallel sentence pair is increased by 2.58. In addition, the retrieval method based on the structure of translation option words graph proposed in this paper is time efficient and has better performance and efficiency in assisting Japanese translation teaching.

scholarly journals Cadlaws – An English–French Parallel Corpus of Legally Equivalent Documents

2021 ◽  
Vol 14 (2) ◽  
pp. 494-508
Author(s):  
Francina Sole-Mauri ◽  
Pilar Sánchez-Gijón ◽  
Antoni Oliver
Keyword(s):  
Machine Translation ◽  
Translation System ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
Legal Documents ◽  
Legal Traditions ◽  
Corpus Construction ◽  
Machine Translation System ◽  
French Corpus ◽  
Language Pair

This article presents Cadlaws, a new English–French corpus built from Canadian legal documents, and describes the corpus construction process and preliminary statistics obtained from it. The corpus contains over 16 million words in each language and includes unique features since it is composed of documents that are legally equivalent in both languages but not the result of a translation. The corpus is built upon enactments co-drafted by two jurists to ensure legal equality of each version and to re­flect the concepts, terms and institutions of two legal traditions. In this article the corpus definition as a parallel corpus instead of a comparable one is also discussed. Cadlaws has been pre-processed for machine translation and baseline Bilingual Evaluation Understudy (bleu), a score for comparing a candidate translation of text to a gold-standard translation of a neural machine translation system. To the best of our knowledge, this is the largest parallel corpus of texts which convey the same meaning in this language pair and is freely available for non-commercial use.

scholarly journals Hindi Chhattisgarhi Machine Translation System Using Statistical Approach

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 02) ◽  
pp. 208-222
Author(s):  
Vikas Pandey ◽  
Dr.M.V. Padmavati ◽  
Dr. Ramesh Kumar
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Statistical Approach ◽  
Statistical Machine Translation ◽  
Target Language ◽  
Translation System ◽  
Parallel Corpus ◽  
Machine Translation System ◽  
Unknown Words ◽  
Language Pair

Machine Translation is a subfield of Natural language Processing (NLP) which uses to translate source language to target language. In this paper an attempt has been made to make a Hindi Chhattisgarhi machine translation system which is based on statistical approach. In the state of Chhattisgarh there is a long awaited need for Hindi to Chhattisgarhi machine translation system for converting Hindi into Chhattisgarhi especially for non Chhattisgarhi speaking people. In order to develop Hindi Chhattisgarhi statistical machine translation system an open source software called Moses is used. Moses is a statistical machine translation system and used to automatically train the translation model for Hindi Chhattisgarhi language pair called as parallel corpus. A collection of structured text to study linguistic properties is called corpus. This machine translation system works on parallel corpus of 40,000 Hindi-Chhattisgarhi bilingual sentences. In order to overcome translation problem related to proper noun and unknown words, a transliteration system is also embedded in it. These sentences are extracted from various domains like stories, novels, text books and news papers etc. This system is tested on 1000 sentences to check the grammatical correctness of sentences and it was found that an accuracy of 75% is achieved.

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