scholarly journals AN OVERVIEW OF AUTOMATED TRANSLATION AND ITS LINGUISTIC PROBLEMS

2021 ◽  
pp. 139-149
Keyword(s):  
Natural Language ◽  
Machine Translation ◽  
Computational Linguistics ◽  
Language Processing ◽  
Information Exchange ◽  
Rapid Development ◽  
Language Translation ◽  
Target Language ◽  
Technical Documentation ◽  
Automated Translation

Language, as the information carrier, has become the most significant means for humans to communicate. However, it has been considered as the barrier of communications between people from different countries. The problem of converting a language quickly and efficiently has become a problem of common concern for humanity. In fact, the demand for language translation has greatly increased in recent times due to effect of cross-regional communication and the need for information exchange. Most material needs to be translated, including scientific and technical documentation, instruction manuals, legal documents, textbooks, publicity leaflets, newspaper reports, etc. The issue is challenging and difficult but mostly it is tedious and repetitive and requires consistency and accuracy. It is becoming difficult for professional translators to meet the increasing demands of translation. In such a situation, the machine translation can be used as a substitute. Machine Translation is the process of converting a natural source language into another natural target language by computer. It is a branch of natural language processing and it has a close relationship with computational linguistics and natural language understanding. With the rapid development of the Internet and the integration of the world economy, how to overcome the barrier of language has become a common problem of the international community. This paper offers an overview of Machine Translation (MT) including the history of MT, linguistic problems of MT, the problem of multiple meanings in MT, syntactic transformations in MT, translation of phraseological combinations in MT systems.

scholarly journals Bodo to English Machine Translation through Transliteration

2019 ◽  
Vol 8 (12) ◽  
pp. 4825-4830
Keyword(s):  
Natural Language ◽  
Machine Translation ◽  
Computational Linguistics ◽  
Language Processing ◽  
Research Work ◽  
Primary Objective ◽  
Target Language ◽  
Translation System ◽  
Hybrid Technique ◽  
High Resource

Machine Translation (MT) is a technique that automatically translates text from one natural language to another using machine like computer. Machine Transliteration (MTn) is also a technique that converts the script of text from source language to target language without changing the pronunciation of the source text. Both the MT and MTn are the challenging research task in the field of Natural Language Processing (NLP) and Computational Linguistics (CL) globally. English is a high resource natural language, whereas Bodo is a low resource natural language. Though Bodo is a recognized language of India; still not much research work has been done on MT and MTn systems due to the low resources. The primary objective of this paper is to develop Bodo to English Machine Translation system with the help of Bodo to English Machine Transliteration system. The Bodo to English MT system has been developed using the Phrase-based Statistical Machine Translation technique for General and News domains of Bodo-English parallel text corpus. The Bodo to English MTn system has been developed using the Hybrid technique for General and News domains of Bodo-English parallel transliterated words/terms. The translation accuracy of the MT system has been evaluated using BLEU technique

Deep Learning

2018 ◽  
Author(s):  
Kyunghyun Cho
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Computational Linguistics ◽  
Language Processing ◽  
Document Classification ◽  
Context Modelling ◽  
Language Modelling

Deep learning has rapidly gained huge popularity among researchers in natural-language processing and computational linguistics in recent years. This chapter gives a comprehensive and detailed overview of recent deep-learning-based approaches to challenging problems in natural-language processing, specifically focusing on document classification, language modelling, and machine translation. At the end of the chapter, new opportunities in natural-language processing made possible by deep learning are discussed, which are multilingual and larger-context modelling.

A Study Of Machine Translation Theories And Methods

2020 ◽  
Vol 2 (09) ◽  
pp. 665-670
Author(s):  
Guli I. Ergasheva ◽  
Zahriddin.X. Haitqulov
Keyword(s):  
Machine Translation ◽  
Information Exchange ◽  
Language Translation ◽  
Technical Documentation ◽  
Study Methods ◽  
Legal Documents ◽  
Statistical Mt ◽  
Methods And Techniques ◽  
History Of ◽  
Need For Information

The demand for language translation has greatly increased in recent times due to increasing cross-regional communication and the need for information exchange. Most material needs to be translated, including scientific and technical documentation, instruction manuals, legal documents, textbooks, publicity leaflets, newspaper reports etc. Some of this work is challenging and difficult but mostly it is tedious and repetitive and requires consistency and accuracy. It is becoming difficult for professional translators to meet the increasing demands of translation. In such a situation the machine translation can be used as a substitute.  This paper intends to study methods and techniques of Machine Translation (MT). Through the following points: History of MT, Statistical MT, Types of MT, and evaluation of MT.  

scholarly journals Research on Discourse Coherence based on the Analysis Model of Event Chain from the Perspective of Computational Linguistics

2020 ◽  
Vol 189 ◽  
pp. 03025
Author(s):  
Min Fan ◽  
Shanwen Xu
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Semantic Analysis ◽  
Rapid Development ◽  
Necessary Condition ◽  
Semantic Structure ◽  
Structure Representation ◽  
Discourse Coherence

With the rapid development of network technology, natural language processing has also entered a boom period. Probability and data-driven methods have been widely used in natural language processing. The need for people to extract and retrieve information from the Internet is also increasing, and more and more researchers are trying to use computers to process content related to discourse coherence. Based on the event chain of the text semantic structure representation, this paper proposes a text semantic structure representation model, on the basis of which, text coherent resources can be used for the task of text semantic analysis. Event chain is a necessary condition for discourse coherence, which can be transformed into a computable event chain analysis problem, and can be further formalized as discourse-oriented partial dependency analysis of sentences.

scholarly journals Arabic-to-Malay Machine Translation Using Transfer Approach

2020 ◽  
Vol 13 (4) ◽  
pp. 1
Author(s):  
Mohammed M. Abu Shquier
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Target Language ◽  
Average Score ◽  
Human Judgment ◽  
Ongoing Research ◽  
Source Language

Translation from/to Arabic has been widely studied recently. This study focuses on the translation of Arabic as a source language (SL) to Malay as a target language (TL). The proposed prototype will be conducted to map the SL ”meaning”with the most equivalent translation in the TL. In this paper, we will investigate Arabic-Malay Machine Translation features (i.e., syntactic, semantic, and morphology), our proposed method aims at building a robust lexical Machine Translation prototype namely (AMMT). The paper proposes an ongoing research for building a successful Arabic-Malay MT engine. Human judgment and bleu evaluation have been used for evaluation purposes, The result of the first experiment prove that our system(AMMT) has outperformed several well-regarded MT systems by an average of 98, while the second experiment shows an average score of 1-gram, 2-gram and 3-gram as 0.90, 0.87 and 0.88 respectively. This result could be considered as a contribution to the domain of natural language processing (NLP).

scholarly journals THE ACCURACY OF GOOGLE TRANSLATION SYSTEM IN ‎TRANSLATING REFERENTIAL PRONOUN (THAT) WITHIN THE ‎LITERARY TEXTS

2021 ◽  
Vol 03 (05) ◽  
pp. 192-201
Author(s):  
Omar Ali Hussein AL-ANI ◽  
Ahmed Adel Nouri AL-ANI
Keyword(s):  
Machine Translation ◽  
Information Exchange ◽  
Language Translation ◽  
Literary Text ◽  
International Communication ◽  
Literary Texts ◽  
Translation System ◽  
Technical Documentation ◽  
Legal Documents ◽  
The Cost

The demand for language translation has greatly increased in recent ‎times due to increasing international communication and the need for ‎information exchange. Most material needs to be translated, including ‎scientific and technical documentation, instruction manuals, legal ‎documents, textbooks, publicity leaflets, newspaper reports etc. Some ‎of this work is challenging and difficult but mostly it is boring and ‎repetitive and requires consistency and accuracy. It is becoming ‎difficult for professional translators to meet the increasing demands of ‎translation. In such a situation the machine translation can be used as ‎a substitute.‎ This study offers a brief but condensed overview of Machine ‎Translation (MT). It aims at identifying the percentages of Google ‎translation system in translating referential pronoun (that) within the ‎literary text. The sample of the study consists of three literary texts. ‎The study used percentages to detect the accuracy of Google ‎translation system. The results showed that percentage of accuracy ‎Google translation is 100% in translating referential pronoun (that) ‎within the literary texts. It’s recommended that Google translation ‎system might be used formally by English teachers and translators in ‎order to get benefit from the time and the cost inside class and ‎translators centers and educational subjects. Keywords: Google, Machine Translation, Human Translation

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

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