Machine Translation Market Size By Technology (SMT, RBMT), By Application (Automotive, Electronics, IT, Military & Defense, Healthcare), Industry Analysis Report, Regional Outlook (U.S., Canada, Germany, UK, France, Spain, China, India, Japan, Brazil, Mexico), Growth Potential, Price Trends, Competitive Market Share & Forecast, 2017 – 2024

Published Date: Apr 2017  |  Report ID: GMI159  |  Authors: Ankita Bhutani, Pallavi Bhardwaj

Report Format: PDF   |   Pages: 120   |   Base Year: 2016




Summary Table of Contents Industry Coverage Methodology

Industry Trends

Machine Translation Market size exceeded USD 400 million in 2016 and is expected to register over 19% growth from 2017 to 2024.
 

Canada Machine Translation Market Share, By Application, 2016 & 2024 

Canada Machine Translation Market Share, By Application, 2016 & 2024

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The rapid growth in the number of business organizations across the globe has created the need to translate large amount of content, augmenting the growth of the machine translation market. Machine translation provides a sophisticated method to translate the content using software technologies. The higher costs associated with human translators are attracting a greater number of businesses to use these translation technologies. The machines are capable of translating the contents at much higher rates than human translators, making them a preferable choice. Furthermore, these technologies can operate in a 24/7 environment, which allows them to translate vast amounts of data in a reduced timeframe.
 

Translation services are advancing with the introduction of new technologies such as Neural Machine Translation (NMT). This technology uses a large neural network with the implementation of artificial intelligence to predict the most suitable sequence of words and sentences. This technology is capable of drastically reducing the post-editing efforts required in the content after translation. NMT is becoming highly popular with major players, such as Google and Alibaba, providing these translation services. However, the given languages to be translated may have completely different structures such as Subject-Verb-Object (SVO) for English and Subject-Object-Verb (SOV) for Tamil language create difficulties in implementing NMT.
 

Machine Translation Market Report Coverage
Report Coverage Details
Base Year: 2016
Historical Data for: 2013 to 2016 Forecast Period: 2017 to 2024
Pages: 120 Tables, Charts & Figures: 103
Geographies covered (11): U.S., Canada, Germany, UK, France, Spain, China, India, Japan, Brazil, Mexico
Segments covered: Technology, Application and Region
Companies covered (19): AppTek LLC, Cloudwords, Inc., Google Inc., IBM Corporation, Lighthouse IP Group, Lingo, Limited, Lingotek, Inc., Lionbridge Technologies, Inc., Microsoft Corporation, Moravia IT, Omniscien Technologies, Lucy Software and Services GmbH, PangeanicMT, PROMT Ltd., SDL PLC, Systran International, Raytheon BBN Technologies, Smart Communications, Inc., Welocalize, Inc.

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By Technology

Statistical machine translation market will see substantial growth in the coming years. SMT technology uses predictive algorithms and statistical analysis for defining the rules most suitable for the required sentence translation. The wide availability of algorithms and platforms is one of the major advantages of SMT, which allows the access of corpus (large and structured set of text) related work at a very cheap rate. The users can also add new languages at a very rapid rate compared to other MT technologies. The less virtual space requirements of SMT technology is enabling their adoption for smaller systems without needing an entire server for operations.
 

Rule-based machine translation technology uses huge number of built-in linguistic rules and bilingual dictionaries for translations. This method provides predictable and consistent translation quality and knows grammatical rules providing robust and high-performance operations. However, the higher costs associated with the customization of developments in this technology are affecting its adoption by users with lower budgets and smaller organizations.
 

By Application

Machine Translation Industry Share, By Application, 2016 (USD Million)

Machine Translation Industry Share, By Application, 2016 (USD Million)
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The rapid growth of the automobile industry is driving the demand for a greater number of translation services. Several dealerships, websites, and companies are increasingly adopting translation services, propelling the machine translation market growth. How-to manuals, detailed vehicle specifications & information, technical documentation, and vehicle part catalogs are majorly translated using MT technologies. The increasing requirements for better marketing communications are making companies to make the data available in several languages to improve customer experiences. For instance, WK Automotive uses KantanMT translation solutions for its scalable commercial translations. The company is aiming to meet customer standards with functionality to refine the content.
 

The penetration of technological giants, such as Google, Baidu, LionBridge, and Systran, is driving the demand in the market for IT applications. The IT companies are highly inclined toward automatic translation technologies for effectively using massive amounts of data generated in the IT industry. The emergence of artificial intelligence and machine learning technologies is enabling more data analytics activities to be automated.
 

By Region

The North America region will have a high contribution to the machine translation market owing to the rapid adoption of new technologies by the U.S. companies. The presence of large tech players providing these services, such as IBM Corporation, Google, and Microsoft, will create higher growth opportunities in the market. These companies are significantly expanding the capabilities of their translation platforms. Moreover, the U.S. is witnessing rapid growth in the number of natural language processing startups, such as TaskUs, SoundHound, and Tact.ai, influencing the application of these technologies in various industries such as customer support, sales, application developments, and others.
 

The penetration of machine learning and intelligent devices enabled improved speech recognition, processing of human languages, and end-to-end translations. The significant reduction in costs, time required, and ease of translating are further attracting a greater number of users in automated translation services in small businesses.
 

Competitive Market Share

Google Inc., IBM Corporation, Lingo24 Limited, Lionbridge Technologies, Inc., Lucy Software & Services GmbH, Raytheon BBN Technologies, Smart Communications, Inc., Welocalize, Inc., Omniscien Technologies, Moravia IT, PangeanicMT, Systran International, Raytheon BBN Technologies, and AppTek LLC are the major companies in the machine translation market. These companies are aggressively developing new translation technologies to eliminate the need for human editing and processing to provide end-to-end solutions. In May 2019, Google introduced its research project ‘Translatotron’, which is capable of converting one spoken language into another without needing intermediate text and works solely using the audio. This technology can also reflect the tone of the speaker’s voice, making it more natural. 
 

The advancements in neural networks and deep learning are making companies to enhance the translation by addressing issues such as wrong meaning. In November 2018, IBM Corporation and Harvard University developed a new tool, allowing the developers of deep learning applications to visualize the AI decision making while translating the sequence of words between two languages.
 

Industry Background

The machine translation gained high popularity with organizations requiring more effective communications with their customers. The preference of people to use native languages and increasing globalization further added to the implementation of software technologies and algorithms to translate the languages. The sectors, such as retail, electronics, and others, are becoming highly competitive and companies are inclined toward establishing their presence with the localization of their brands.
 

Many of the languages require prior experience and knowledge for proper translations and to keep the required context and meaning. In addition, the raw translation methods require large amounts of post-editing. Owing to these issues, some of the companies requiring translation services are still considerably preferring human translators, which can restrain market growth.
 


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