Summary

Machine Translation Market Growth & Trends

The global machine translation market size is expected to reach USD 2,719.0 million by 2030, expanding at a CAGR of 13.5% from 2023 to 2030, according to a new report by Grand View Research, Inc. The reinvention of computer-aided translational tools and growth of adaptive machine translation has considerably widened the scope of the machine translation market.

Key players are increasing their focus on developing advanced MT systems to meet growing consumer needs. For instance, Lilt Inc. has launched an adaptive neural machine translation system to use real-time feedback loop and Artificial Intelligence (AI) to boost the productivity of a human translator. Moreover, the technology is expected to offer opportunities to people for communicating and accessing information in a particular language. This system goes beyond Facebook, Inc.; Apple Inc.; and Microsoft Corporation’s product offerings.

Globalization has significantly contributed to addressing the need for linguistically diverse local audiences. Growing importance of businesses in emerging markets, creation of online content, and need for enabling global collaboration among employees is expected to drive market growth over the forecast period.

Need for organizations to localize their content in several languages to capture international markets is expected to bolster demand for MT systems. Adoption of such systems and instant web publishing requirements is further expected to improve user experience by developing multilingual engagement across different content types.

Growing importance of cloud computing has resulted in increasing demand for cloud-based translation tools. Moreover, delivery of Machine Translation Software-as-a-Service (MTSaaS) over secure cloud offerings accommodated in data centers is expected to offer considerable growth opportunities.

The convergence of Translation Memories (TM) and MT is expected to fully integrate workflows. TM store sentences and paragraphs that were previously translated to aid human translators, accelerating translation projects and reducing costs. Furthermore, the industry has gravitated toward the use of Bilingual Evaluation Understudy (BLEU) as a useful measurement of translation quality. BLEU is a method for automatic evaluation of MT, which is inexpensive and language independent.

Machine Translation Market Report Highlights

• Existence of a large number of players and government initiatives supporting machine translation has impacted industry growth positively in U.S. Google Inc. and Microsoft Corporation have made SMT technology popular with their free online engines in the country

• Demand for Statistical Machine Translation (SMT) has significantly increased over the past few years, due toits effectiveness over Rule-Based Machine Translation (RBMT) in terms of time and cost

• The statistical machine translation (SMT) contributed to more than 72.5% of the overall MT market revenue

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Table of Contents

Table of Contents

Chapter 1. Methodology and Scope
1.1. Market Segmentation & Scope
1.1.1. Technology
1.1.2. Application
1.1.3. Regional scope
1.1.4. Estimates and forecast timeline
1.2. Research Methodology
1.3. Information Procurement
1.3.1. Purchased database
1.3.2. GVR’s internal database
1.3.3. Secondary sources
1.3.4. Primary research
1.3.5. Details of primary research
1.4. Information or Data Analysis
1.5. Market Formulation & Validation
1.6. Model Details
1.7. List of Secondary Sources
1.8. List of Primary Sources
1.9. Objectives
Chapter 2. Executive Summary
2.1. Market Outlook
2.2. Segment Outlook
2.2.1. Technology outlook
2.2.2. Application outlook
2.2.3. Regional outlook
2.3. Competitive Insights
Chapter 3. Machine Translation (MT) Market Variables, Trends & Scope
3.1. Market Lineage Outlook
3.2. Industry Value Chain Analysis
3.3. Market Dynamics
3.3.1. Market driver analysis
3.3.2. Market restraint analysis
3.3.3. Market opportunity analysis
3.4. Machine Translation (MT) Market Analysis Tools
3.4.1. Industry analysis - Porter’s
3.4.1.1. Supplier power
3.4.1.2. Buyer power
3.4.1.3. Substitution threat
3.4.1.4. Threat of new entrant
3.4.1.5. Competitive rivalry
3.4.2. PESTEL analysis
3.4.2.1. Political landscape
3.4.2.2. Technological landscape
3.4.2.3. Economic landscape
Chapter 4. Machine Translation (MT) Market: Technology Estimates & Trend Analysis
4.1. Machine Translation (MT) Market: Key Takeaways
4.2. Machine Translation (MT) Market: Movement & Market Share Analysis, 2022 & 2030
4.3. RBMT
4.3.1. RBMT market estimates and forecasts, 2017 to 2030 (USD Million)
4.4. SMT
4.4.1. SMT market estimates and forecasts, 2017 to 2030 (USD Million)
4.5. Others
4.5.1. Others market estimates and forecasts, 2017 to 2030 (USD Million)
Chapter 5. Machine Translation (MT) Market: Application Estimates & Trend Analysis
5.1. Machine Translation (MT) Market: Key Takeaways
5.2. Machine Translation (MT) Market: Movement & Market Share Analysis, 2022 & 2030
5.3. Automotive
5.3.1. Automotive market estimates and forecasts, 2017 to 2030 (USD Million)
5.4. Military & Defense
5.4.1. Military & defense delivery market estimates and forecasts, 2017 to 2030 (USD Million)
5.5. Electronics
5.5.1. Electronics market estimates and forecasts, 2017 to 2030 (USD Million)
5.6. IT
5.6.1. IT market estimates and forecasts, 2017 to 2030 (USD Million)
5.7. Healthcare
5.7.1. Healthcare market estimates and forecasts, 2017 to 2030 (USD Million)
5.8. Others
5.8.1. Others market estimates and forecasts, 2017 to 2030 (USD Million)
Chapter 6. Machine Translation (MT) Market: Regional Estimates & Trend Analysis
6.1. Regional Outlook
6.2. Machine Translation (MT) Market by Region: Key Takeaway
6.3. North America
6.3.1. Market estimates and forecasts, 2017 to 2030 (Revenue, USD Million)
6.3.2. U.S.
6.3.2.1. Market estimates and forecasts, 2017 to 2030 (Revenue, USD Million)
6.3.3. Canada
6.3.3.1. Market estimates and forecasts, 2017 to 2030 (Revenue, USD Million)
6.4. Europe
6.4.1. UK
6.4.1.1. Market estimates and forecasts, 2017 to 2030 (Revenue, USD Million)
6.4.2. Germany
6.4.2.1. Market estimates and forecasts, 2017 to 2030 (Revenue, USD Million)
6.4.3. France
6.4.3.1. Market estimates and forecasts, 2017 to 2030 (Revenue, USD Million)
6.5. Asia Pacific
6.5.1. Japan
6.5.1.1. Market estimates and forecasts, 2017 to 2030 (Revenue, USD Million)
6.5.2. China
6.5.2.1. Market estimates and forecasts, 2017 to 2030 (Revenue, USD Million)
6.5.3. India
6.5.3.1. Market estimates and forecasts, 2017 to 2030 (Revenue, USD Million)
6.5.4. Australia
6.5.4.1. Market estimates and forecasts, 2017 to 2030 (Revenue, USD Million)
6.5.5. South Korea
6.5.5.1. Market estimates and forecasts, 2017 to 2030 (Revenue, USD Million)
6.6. Latin America
6.6.1. Brazil
6.6.1.1. Market estimates and forecasts, 2017 to 2030 (Revenue, USD Million)
6.6.2. Mexico
6.6.2.1. Market estimates and forecasts, 2017 to 2030 (Revenue, USD Million)
6.7. MEA
6.7.1. Saudi Arabia
6.7.1.1. Market estimates and forecasts, 2017 to 2030 (Revenue, USD Million)
6.7.2. South Africa
6.7.2.1. Market estimates and forecasts, 2017 to 2030 (Revenue, USD Million)
6.7.3. UAE
6.7.3.1. Market estimates and forecasts, 2017 to 2030 (Revenue, USD Million)
Chapter 7. Competitive Landscape
7.1. Recent Developments & Impact Analysis, By Key Market Participants
7.2. Market Participant Categorization
7.2.1. AppTek
7.2.1.1. Company overview
7.2.1.2. Financial performance
7.2.1.3. Product benchmarking
7.2.1.4. Strategic initiatives
7.2.2. Cloudwords Inc.
7.2.2.1. Company overview
7.2.2.2. Financial performance
7.2.2.3. Product benchmarking
7.2.2.4. Strategic initiatives
7.2.3. IBM Corporation
7.2.3.1. Company overview
7.2.3.2. Financial performance
7.2.3.3. Product benchmarking
7.2.3.4. Strategic initiatives
7.2.4. Lighthouse IP
7.2.4.1. Company overview
7.2.4.2. Financial performance
7.2.4.3. Product benchmarking
7.2.4.4. Strategic initiatives
7.2.5. Lingo24 Limited
7.2.5.1. Company overview
7.2.5.2. Financial performance
7.2.5.3. Product benchmarking
7.2.5.4. Strategic initiatives
7.2.6. Lingotek
7.2.6.1. Company overview
7.2.6.2. Financial performance
7.2.6.3. Product benchmarking
7.2.6.4. Strategic initiatives
7.2.7. Lionbridge Technologies, LLC.
7.2.7.1. Company overview
7.2.7.2. Financial performance
7.2.7.3. Product benchmarking
7.2.7.4. Strategic initiatives
7.2.8. RWS Holdings plc
7.2.8.1. Company overview
7.2.8.2. Financial performance
7.2.8.3. Product benchmarking
7.2.8.4. Strategic initiatives
7.2.9. Pangeanic
7.2.9.1. Company overview
7.2.9.2. Financial performance
7.2.9.3. Product benchmarking
7.2.9.4. Strategic initiatives
7.2.10. PROMT
7.2.10.1. Company overview
7.2.10.2. Financial performance
7.2.10.3. Product benchmarking
7.2.10.4. Strategic initiatives
7.2.11. RTX
7.2.11.1. Company overview
7.2.11.2. Financial performance
7.2.11.3. Product benchmarking
7.2.11.4. Strategic initiatives
7.2.12. Smart Communications
7.2.12.1. Company overview
7.2.12.2. Financial performance
7.2.12.3. Product benchmarking
7.2.12.4. Strategic initiatives
7.2.13. SYSTRAN
7.2.13.1. Company overview
7.2.13.2. Financial performance
7.2.13.3. Product benchmarking
7.2.13.4. Strategic initiatives
7.2.14. Welocalize
7.2.14.1. Company overview
7.2.14.2. Financial performance
7.2.14.3. Product benchmarking
7.2.14.4. Strategic initiatives