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Written by Steven Bussey
on September 28, 2021

Learn how to choose the best Machine Translation engine to speed up your process, increase accuracy, and save costs, and see the advantages of integrated translation solutions.

Table of Contents 

1.  Introduction to Machine Translation
2.  What is Machine Translation? 
3.  Benefits of Machine Translation Software
4.  Disadvantages of Machine Translation

5.  Machine Translation vs. Human
6.  Advantages of Language Studio Machine Translation by Andovar 
7.  History of Machine Translation
8.  Machine Translation Use Cases
9.  Machine Translation Software Options
10. Machine Translation Platform Implementation
11. Measuring Machine Translation Quality
12. Security Concerns for Corporations
13. Which Machine Translation Software is Right For Your Company?
14. FAQ About Choosing Machine Translation Software
15. Andovar Takes Your Content Global

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1. How to Choose Machine Translation Software

Choosing the right Machine Translation (MT) software for your industry and content type can speed up your workflow, increase accuracy rates, and reduce costs, all by stunning percentages! 

Today's best Machine Translation software platforms are increasingly used by top professional localization services providers (LSPs). MT solutions can facilitate faster production, increased translation quality, and reduced costs. Some Machine Translation software programs are becoming standard integrations for professional translation and quality testing. There are various Machine Translation  options to suit various industry- and company-specific needs. Choosing the best machine translation engine depends on a small group of large factors that will affect the outcome of your project.       

Further, there are important advantages of customized Machine Translation platforms and integrated translation solutions, like Andovar’s customized Language Studio MT software by Omnicien. This specialized Machine Translation  resource is the centerpiece of the comprehensively integrated MT arm of our language translation and larger localization process. Let’s look at the things you should consider when selecting Machine Translation technology for your particular use.

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2. What is Machine Translation?

Machine Translation (MT) is the application of automated AI-facilitated software for translating spoken or text language. MT is used to translate with human translators performing post-editing (PE). Basic Machine Translation MT software simply performs word-for-word substitutions. More advanced Machine Translation MT technology operates with rule-based or statistical translation models, delivering more accurate outcomes. Some advanced Machine Translation products automatically employ specific bases of terms and innovative techniques for analysis of grammar, syntax, and semantic elements to translate text.  

There are many useful, basic, public free Machine Translation engines available online, such as Google Translate, Amazon Translate, and DeepL. Machine translation software platforms in this class are understood as less than reliable. However, overall, MT tech quality has evolved so much that it has become a fundamental tool for top location service providers (LSPs). When used with skilled human translators for post-editing, professional-grade Machine Translation software is widely used for interpreting localized content and assessing needs for modifications. 
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3. Benefits of Machine Translation Software 👍

Machine Translation is often less appreciated than it deserves when people compare it to the quality of human translation work. The problem lies in uninformed expectations, not in the performance capacity of Machine Translation. Employing MT software is not intended to replace human translators. Its function is largely as an aid to professional translators. So, there is a need for a new way of thinking about how much Machine Translation contributes to the success of large localization projects.  

When machine translation software is integrated with a computer-assisted translation (CAT) platform, the powerful CAT tool capabilities are multiplied. This high-performance combination of translation technologies is used by global organizations that need to manage very large and complex localization projects with exceptional efficiency.  

The benefits of Machine Translation include: 

  • Speed: Machine Translation software translates at incomparable rates of output.  
  • Reliability: Machine Translation is accurate enough for translating commonly used words and expressions. 
  • Customizable: Workflows can be modified to accommodate specialized industry or client needs. 
  • Multitasking: Machine Translation automatically translates to multiple languages simultaneously. 
  • Cost Savings: Machine translations cost a fraction of human translation services. 
  • Unburdens Translators: Frees human translators to focus on the finer details of translation. 
  • Integratable: Machine Translation seamlessly integrates with CAT and other localization systems.

4. Disadvantages of Machine Translation 👎 

Machine translation software offers a long list of pros (above), but like any other complex technology, it naturally also comes with its cons:  

  • Imperfect: Not all Machine Translation translations are precise matches with the original content.  
  • Generic: A software program cannot comprehend cultural or contextual nuances, so Machine Translation cannot accurately translate those aspects of content. 
  • Developing: Translation quality in some languages is less than in others, due to development levels in termbases, glossaries, customizations, and integration.

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5. Machine Translation vs. Human 

Where the concerns about machine translation are often in error is in the assumption that professional users of Machine Translation cannot expect accuracy without post-editing. The point here is that, in fact, both the Machine Translation and post-editing phases of the process are understood by LPS project managers as necessary and built-in stages of their standard workflow.  

In complex projects translating large volumes of content and low-profile content, Machine Translation has become indispensable. Its superior cost-effectiveness makes it an appealing option for any localization client prioritizing value. 

In fact, although in the vast majority of machine translation projects, post-editing is necessary, there are instances when only light post-editing, or even no post-editing is needed. Whether PE will be needed for MT depends on these three factors:: 

  1. The quality of the Machine Translation output  
  2. The extent of the engine training 
  3. The robustness of the corpus 
  4. Content structure 
  5. Target language 
  6. Availability and extensiveness of the termbase 
  7. How well aligned users’ expectations are with the performance potential of the Machine Translation engine 

In some cases, clients can opt for ultra-light PE. For example, in some e-commerce product descriptions where the availability of content in the target language outweighs the issue of some translation errors, applying minimal post-editing may yield greater value for the translation project.  

Why is PEMT Necessary with Machine Translation? 

Machine translation makes localizing your content much faster and easier, which means it saves significant costs for your business. It also helps in meeting tight timeline objectives. Post-edited machine translation (PEMT) is essential in most projects involving Machine Translation, to capture mistakes and help ensure the meaning of the message is conveyed to people of a different language and cultural experience.  

In addition to ensuring that the intention of the original message is clearly expressed in the localized version, PEMT achieves precision in translation accuracy. This provides a professionally polished product for you. With the rise in the use of Machine Translation, the need for PEMT increases.  

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6. Advantages of Language Studio
Machine            Translation by Andovar  

A state-of-the-art Machine Translation platform called Language Studio has integrated translation engines that can be specifically trained for your industry. Language Studio, adopted as Andovar’s default MT engine for our batch translation workflows, offers important advantages for our clients

Language Studio is an enterprise-class hybrid Machine Translation platform featuring state-of-the-art AI-empowered Deep Neural Machine Translation (DNMT) and  conventional Statistical Machine Translation (SMT). Combining these processes yields unsurpassed machine translation quality. Andovar’s MT partner, Omnicien, has developed customized NMT engines and created a toolset for preparing content for Machine Translation and further improving output quality. 

Neural MT utilizes machine learning (ML). NMT systems continuously learn and adjust to provide the best output. Of course, as with any ML-capacitated technology, processing the right data generates better outcomes. Language Studio features technology for gathering, processing, and synthesizing the data necessary for self-training. With input of adequate data, NMT can think more similarly to a human brain in processing language translations.  

Far surpassing the databases of typical SMT engines, an NMT engine may draw from over 50 million bilingual sentences, just as a baseline. Omniscien supplies a great percentage of the needed data, mines additional data, and synthesizes new bilingual sentences in the millions for every engine it customizes. All together, these advancements make the integrated Language Studio platform today’s best machine translation software solution. 

Because there remain certain instances in which SMT produces better quality results than NMT, Andovar utilizes the Language Studio to seamlessly integrate the capabilities of both technologies for the highest possible quality of translation

The software is designed for maximum security, data privacy, scalability, flexibility, and user control. Language Studio integrates the power of Workflow Studio functionality directly into the translation workflow.  

Some of the many features of Language Studio include: 

  • Customizable  
  • 500% faster translation 
  • On-premises 
  • Data-center platform 
  • Advanced data preparation tools 
  • Intuitive user interface 
  • Improved translation rules for run time 
  • Expanded translation workflow 
  • Enhanced confidence scoring 
  • Added file sources (email, Dropbox, AWS S3, FTP, etc.) 
  • Integrates with Workflow Studio 

Andovar’s Integrated Machine Translation Solutions 

Our translation process features Language Studio, a customizable Machine Translation software solution for corporate projects utilizing our secure cloud and on-premises platform server options. We also utilize various other CAT-compatible ready-to-use MT engines in an optimally integrated enterprise-grade translation system.   

Corporate in-house translation operations are well-advised to move to machine translation software solutions that integrate a comprehensive mix of Machine Translation engines, after the Andovar model. Our system integrates multiple ready-to-use MT engines, such as those listed below, in our integrated offering.  

By integrating a wide range of machine translation software products, we automatically capture those that work best for each language, purpose, content type, and compliance need. For example, the Andovar integrated MT solution features: 

  • Google (Cloud Translate API) 
  • DeepL Translate 
  • Amazon Translate (AWS) 
  • Other general-purpose Machine Translation engines 
  • Customizable Machine Translation engines  

Some other ready-to-use Machine Translation engines: 

  • Microsoft Translator 
  • Systrans 
  • Bing Translator 
  • IBM Watson Language Translator 
  • PONS Online Translator 
  • PROMT Translator 
  • Hybrids 

Content Triage 

Frequently, even within a single file, not all content is equally suitable for machine translation. So, Andover utilizes artificial intelligence (AI) for Confidence Scoring in a process called Machine Translation Quality Estimation (MTQE). This assessment functionality is built into our Translation Management System (TMS).  

In addition to enabling the selection of the best MT engine for a particular project, Memsource identifies segments of content that can be processed by MT. The system goes much further to predict which parts of the content probably will need only light post-editing or no PE, and which are likely to need 100% human translation. This system gives Andover clients the advantage of an exceptionally efficient apparatus for quality management of MT engines.  

In addition to the MQM, MTQE framework built into the powerful Memsource Translation Management System, we also use this versatile system for measuring project output.  

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7. History of Machine Translation

The initial attempts at machine translation reportedly happened back in the 1940s. In the early 1950s, the concept attracted more interest, underwent a surge of development in the 1990s, and has continued evolving since then. The Machine Translation market is now anticipated to grow to over USD $600 million in North America alone by 2024 and reportedly between USD $943M to as high as USD$1.5B globally that same year by some estimates. (The U.S. currently accounts for over 60% of the global localization market.)  

The globalization of the commercial marketplace now relies on MT technology. More and more e-commerce companies and other international enterprises are now relying on machine translation software to facilitate the work of human translators in localization processes

The Evolution of Machine Translation

Modern adaptive AI-enhanced MT systems perform real-time updates driven by content edits. This means the best machine translation software is continuously learning and building on its knowledge base. Current MT technology types include: 

  • Rule-Based Machine Translation (RBMT): Developed many years ago as the first practical Machine Translation technology, RBMT parses source content segments to interpret words, analyze sentence structures, and translate them based on rules set by linguistics experts. The rules are applied by the system to define correlations between structures in the source and target languages. 
  • Statistical Machine Translation (SMT): SMT is the processing system used by popular online free platforms like Bing Translator and Google Translate. It is today’s most commonly used MT technology. SMT searches segments of source texts and potential translations and phrases within the segments for statistical correlations, to develop models for translations. The system then calculates confidence scores, evaluating the likelihood that the source text will match the translation to be rendered. 
  • Neural Machine Translation (NMT): NMT marks a technological paradigm shift in machine translation. Today’s state-of-the-art Neural Machine Translation engine is an advanced form of MT that consists of an artificial neural network with artificial intelligence training it. The system is designed to predict word sequences, extrapolate from accumulated information, and generate translated sentences that are modeled from the results.  

    In contrast to the conventional SMT translation system consisting of numerous separately adapted components, NMT is built and trained as a singular neural network that reads and translates sentences. All parts of the system are jointly trained from end to end, to maximize translation performance. 
  • Deep Neural Machine Translation (DNMT): First-generation NMT, with its single layer of neural network language translation processing, has further evolved into Deep NMT (DNMT). This version of neural machine translation design features multiple stacked neural processing layers. This means there are many more jointly trained processing elements maximizing translation performance compared to the early NMT engines. 

In most circumstances, NMT generates translations of much higher quality than SMT while using just a fraction of memory. NMT is becoming increasingly important in localization. Currently, this technology is mostly used by leading global LSPs. However, the spectacular NMT innovation is projected to become more accessible and more widely used. 

The Current State of Machine Translation  

As far as MT has evolved, even to the marvelous depths of the DNMT model’s artificial neurons and their amazing human-like functionality, MT nevertheless is not yet a technology that has reached its maximum potential. Generally speaking, at this point, the world’s best machine translation software has advanced to serve these common translating purposes: 

  • Gisting: Translating to a generalized version of what the source text says, representing only the essence of the message, without delivering the benefit of a richer expression of it.  
  • Immediate Need: Translating content that cannot wait for more time-consuming human translation, such as for texts, chat, etc. 
  • MT/human Translation: Humans perform post-editing of machine translations to produce error-free, stylistically appropriate final versions of content. 
  • Controlled Language: Customized Machine Translation platforms provide exceptionally high-quality translations of content written in controlled language, for example, certain reports, specifications, and various other documentation. 
  • High Volume: Machine Translation generates great volumes of translations at lightning speed when human translation alone is not feasible economically or technically. 
  • Pseudo-Translation: Localization specialists can apply Machine Translation to compare source text with target languages, to examine for internationalization issues prior to undertaking translation. 

Although this emphasizes the limitations of Machine Translation without human post-editing, again, there are cases in which very little if any PE is needed. Again, the extent of need for PE can depend on the MT output quality, the termbase, and the user's expectations.  

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8. Machine Translation Use Cases

By all accounts, Machine Translation is not perfect. It comes with its good and bad aspects. The takeaway point here is that the MT option should be weighed for all sizeable translation projects. You should decide to use it or dismiss it for each project based on your results and not on preconceptions based on anecdotal reports. 

The primary use cases for machine translation are: 1) processes that necessitate rapid interaction, such as assimilating web chat or texts, and 2) as a tool to increase the productivity of human translators

Use these general guidelines for the use of MT: 

Good Fit with MT Not as Good Fit with MT Natural language Unstructured content Nuanced Contextual Longer sentences High stakes Literature, marketing content Informal Nonprofessional content Controlled language Struc

9. Machine Translation Software Options

All the basic Machine Translation software programs listed below integrate with Memsource. These platforms auto-translate verbiage between more than 600 language pairs. This makes these basic MT resources good content translation aids. When coupled with post-editing by human translators, Machine Translation helps elevate translated content to meet the highest standards for human translation quickly, and at a reduced cost. 

Options for Machine Translation software types include: 

  • Rule-Based Machine Translation (RBMT): RBMT solutions operate by rules that are based on the software’s respective analyses of the source and target languages. 
  • Statistical Machine Translation (SMT): SMT software utilizes an array of separate components enabling algorithms and statistical models to create translations after analyzing substantial amounts of data. 
  • Example-Based Machine Translation (EBMT): EBMT systems translate sentences by retrieving and comparing similar or matching existing translation source sentences and targets as interpretive examples for processing the current translation task.  
  • Neural Machine Translation (NMT): NMT uses a large single artificial neural network of functions jointly trained for end-to-end maximization of translation performance. 
  • Hybrid Method: Combine options and build a system that enables you to select the best choice to match the content type and other considerations for a given Machine Translation project.  

Hybrid MT platform models combine the capacity of services like AWS, Google Translate, DeepL, Amazon Translate, and customized MT such as Language Studio. These tools can be integrated into workflows in AI-facilitated Memsource.  

Another alternative is to use a platform product like Memsource, which automatically identifies and selects the best machine translation software solution for your current purposes, based on factors including language pairs, content type, and domain, among others.  

Machine Translation platform model options: 

  • Language-Specific MT Solutions: For example, DeepL is a good choice for various European to/from Asian language translations 
  • Customized MT Platforms: Like Language Studio customized for Andovar, with over 600 language pairs. Key features of a customized MT system may feature:  
  • Ready-made MT engines: Use Google Translate, Amazon Translate, or other publicly available free MT services. These do not have advanced functions or customization, and your data can be reused in the providers’ other services.  
  • Other custom MT engines: Platforms designed for processing in certain industries, for specific language pairs, particular content types, and other defined needs and outcomes.  
  • Cutting-Edge AI Deep Neural MT: State-of-the-art DNMT technology for the fastest and most accurate performance in broad-scale translation for localization.  
  • Cloud MT: Similar functionality to free public MT engines, hosted in the cloud,  but provides a dedicated account for exclusive use by your company. Cloud MT provides added capabilities in terminology customization, plus various other benefits. Your data with the service is well-secured. OR: 
  • On-Premises MT: For companies that plan to deploy machine translation software in their in-house IT sphere. This is an exceptionally secure approach, but the cost is significant, deploying and managing the system is complex and requires continuous maintenance. 
  • Best of Breed MT: This is a platform that enables the management of multiple MT engines, provides one layer of term customization, and features a conveniently manageable UI. It allows you to select the best MT engine(s) for various content types and language pairs.  
  • REST API: The REST API protocol is the common preference for flexible integration, simplicity, and ease of use.  

Machine Translation technology has made spectacular progress over recent years, and many new language pairs have been added to various MT products. Still, the free MT services are currently useful for conveying only the gist of messages. Translations for casual use are sufficient, but translating content for commercial purposes requires post-editing by human translators and localization specialists.  

NOTE: Machine Translation is not the same as CAT (Computer-Aided Translation). CAT tools enable collaboration between multiple translators and groups and integrate multiple tech components on one platform, like document editors, Quality Assurance, MT software, and others. CATs are designed to maximize the productivity and consistency of translation. 

When Should You Use Machine Translation? 

For some translation projects, Machine Translation alone may be sufficient, but for a majority of content, combining MT and human translation is necessary for good results. In some cases, MT is not useful at all. So, Machine Translation is not meant to be treated as a one-size-fits-all solution. 

For projects with large volumes of content for translation in a short timeframe, MT offers an excellent tool for boosting productivity rates. However, some extent of human post-editing must be applied, even when precision is not required, for example, in internal company documents, news monitoring feeds, content containing customer reviews, and other non-public-facing materials. 

Another criterion for judging a project as a good candidate for Machine Translation application is the level of nuance or complexity of the information in the content. In high-volume projects with these translation challenges, combining MT with post-editing vastly expands the opportunities to use MT for speed and cost savings. 


10. Machine Translation Platform Implementation

Machine Translation engines generate translations virtually instantly, and accuracy rates continue to increase with AI and with further development of the technology. Currently, the most accurate, efficient, and economical way to localize content is with MT plus human post-editing (MTPE). Successful Machine Translation implementation implementation requires following a thorough process. Here are the basic 7 steps for implementing MT engines

  1. Prioritize Data Security Rules: Not all MT engines are compliant with GDPR or HIPAA. If customer data to be handled through your translation system requires protection, ensure that the machine translation software you select provides it. 

  2. Process Content Suitable for MT: Some types of content are more compatible with MT engines than others. For the best results, use MT for content that is structured and straightforward in form. Professionally written FAQs, general customer service information, etc. 

  3. Train Your MT Engine: Train the MT engine with words, phrases, and other content elements your company frequently uses. Accurate machine translations require a minimum of 100,000 segments. You can build or buy corpora (text collections), or obtain it from public sources for use in training your MT engine with data relevant to your industry. 

  4. Recruit Post-Editors: Apply post-editing to ensure accuracy after machine translation. Use light editing for glaring content translation issues, or use full editing to correct any mistakes, including cultural errors. Ensure consistency in the application of MT post-editing process management. 

  5. Sample in Advance: The purpose of choosing the best machine translation engine is to save time and money. But, if the results are bad, MT can cost you more instead of less. So, test to confirm that the quality will be sufficient to send your translated content forward for post-editing. 

  6. Get Pricing Upfront: As with any investment, obtain an agreement on price from all the stakeholders before you commit. Use MT engines that generate quality estimations (MTQE), to help you determine the most accurate cost estimate. 

  7. Roll It Out: Your machine translation outcomes may not meet your standards initially. But, by continuing to train the engine, the results will improve. With some fine-tuning, you will achieve the level of efficiency you’re aiming for. 

Remember that post-editing is an integral part of the success of translation processes that rely on MT and require both volume output and high accuracy. Andovar turnkey translation solutions, for example, includes both MT and post-editing components. Our technology is set up to auto-forward segments of content to either the MT engines or to human translators, as most appropriate.  

Before we begin MT implementation, we utilize MTQE scores that indicate the level of accuracy to be expected in the MT output. This allows us to correctly estimate the cost to help you go global with your content.  


11. Measuring Machine Translation Quality

The accuracy of a large-scale translation project mostly depends on the MT engine you choose. Currently, the most accurate method for evaluating the quality of MT output is for human evaluators to score sentence-by-sentence. Additionally, auto-evaluation methods are used to measure consistency between MT and human translations. For example: 

  • Word Error Rate (WER): Based on the number of insertions, deletions, and substitutions made to translate the reference sentence, sometimes measured by the resulting edit distance alone.  
  • Position-Independent Error Rate (PER): Computes the WER by recognizing sentences as clusters of words and disregarding the word order. 
  • Rank-based Intuitive Bilingual Evaluation Scores (RIBES): Based on analysis of the reordering of words.  
  • Bilingual Evaluation Understudy (BLEU): Measures similarity of MT output to a set of high-quality reference translations. 
  • Metric for Evaluation of Translation with Explicit Ordering (METEOR): Considers word stems and synonyms. 

Frequently used QA testing tools for MT include Xbench, Verifika, ServiceNow, and others, and CAT platforms that feature QA tools for localization.  

A preliminary QA testing process that is recommended by LSP QA testing experts, apart from the industry’s standard final full-scope QA testing is QA spot-checking. MT is typically used in this kind of supplementary localization QA process.   

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12. Security Concerns for Corporations

Translating company documents, internal communications, and protected customer information, and other materials can present serious cybersecurity risks. To tighten data security in translation processes, examine your company’s language translation activities, and assess practices that may be exposing sensitive information. Here are some areas of security concern to be aware of as you shop for the best machine translation software platform for your company’s needs: 

  • Data returns to online MT engines when using those free translation tools 
  • Disregarding user permission controls  
  • Transmitting files for translation as email attachments 
  • Failure to use translation memory group permissions 
  • Encrypted file storage 
  • 256-bit SSL certification 
  • SHA-2 and 4096-Bit Encryption 
  • Device verification 
  • Transport layer security 
  • Two-step authentication 
  • Compliance with specified mandates 
  • Compliant translation data centers 
  • Third-party security assessment records 
  • Updated browser 
  • Automatic logoff 
  • Last login information 

To remedy these security issues, use a CAT tool that enables first-draft translations in the MT process without enabling data access to the free MT engine provider. Choose a secure MT platform with robust project management capabilities. Ensure that data transitions can be centralized and user permission controls and translation memory group permission controls are active.  

If the machine translation software you’re considering does not have the above security controls, you should move on to find a secure translation platform that features all the above security measures. Further, keep in mind that for data protection, on-premises MT applications are safest, secure cloud products are safe, and free online MT engines are the least likely to be GDPR compliant and the least secure. 

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13. Which Machine Translation Software is Right for Your Company?

There are numerous MT options, and as you have probably gathered by now, there is not an MT engine specifically designed for any particular content typeGeneric MT engines can translate most kinds of content. But, with a custom MT platform, you can tailor training data to your industry and content type.  

But, which is the best machine translation software for your company? The answer depends on a set of factors including these, among others:  

  • MT Software Type: First, familiarize yourself with the classes of MT engines and generally how they work to process language translations.  
  • Your Content Type: Decide which type of content you want to translate. Test your results for some content samples through the MT and post-editing process.  
  • Your Industry Type: Some global industry types involve translation of voluminous complex technical language that requires the highly sophisticated processing provided by Neural MT. 
  • Desired Language Pairings: Statistical MT may offer adequate translation for your needs. 
  • Data Volume: Neural MT requires large amounts of text to learn and deliver benefits. 
  • MT Security Types: Scrutinize the MT software provider’s privacy and security policies. 
  • TMS Compatibility: Be sure your chosen MT software is supported by your TMS.  
  • Budget: Neural MT is more expensive to train than statistical MT.  

For best results, use a TMS that automatically selects the best machine translation engine for your particular needs in any given project. Again, there is no particular machine translation software with functionality specifically designed to match your content type perfectly. But, the best MT engines can be trained for particular data types and subjects.  

Your Need for an Integrated Translation Solution 

Overall, an integrated translation solution offers the broadest coverage of the range of potential language translation needs. This option also helps ensure the greatest efficiency, accuracy, and cost-effectiveness of your localization process

For the range of reasons discussed in the sections above, you should move toward an integrated MT solution that provides you with a practical mix of the best machine translation software products. Different engines are best for different languages, content types, industry types, etc., so using the best-of-breed solution discussed above gives you what you need for particular projects automatically. That means you have complete coverage by all the best MT software options managed for you with the incomparable convenience of an automatic MT engine evaluation and selection process.  

Your Need for Post-Editing 

A post-editor applies localization industry best practices in reviewing the accuracy and readability of the output from your MT software. The post-editor ensures that all the terms and phrasing, etc., meet your quality standard for the project. To determine how much post-editing makes sense for your purposes, weigh your priorities for quality in the particular content against your time and cost limitations.  

In light post-editing, the editor focuses only on significant errors that cause the translated content to be wrong, offensive, or illegible. In full post-editing, the reviewer scrutinizes the machine-translated content for any errors, including grammar or tonal issues, or cultural inaccuracies. This PEMT formula proves to be consistently successful. 


MT technology is continuously advancing toward a higher quality of translations. As growing global companies produce increasing amounts of content for consumption in multiple languages and cultures, MT-aided translation and localization are extending their international reach. MT software accelerates the translation process and saves costs.  

Because even today’s best machine translation software lacks the functional capacity to recognize national and local cultural and social differences, human post-editing remains essential for accurate translation and effective localization. Further, MT engines are frequently not precisely accurate, especially in capturing language nuance. Although MT is not ideally intuitive, the technology is extraordinarily useful in translating large volumes of content into reasonably comprehensible outputs. 

Using a CAT system that efficiently integrates MT with human reviewer processes elevates the quality of outcomes and generates the highest success rate in terms of translation accuracy. It is this blended approach that consistently produces the best results for LPS clients, and it’s the solution that the Andovar global team believes in and leverages to benefit our clients. Andovar’s worldwide team of expert post-editors and localization project management professionals fine-tune your translated content to ensure it meets your needs. 

But, in establishing machine translation operations for your company, the driver of your decision on MT type and the particulars of the translation system you’ll build is the value of your content. The type and volume of content you need to translate should be the overarching concerns in choosing the best machine translation engine and developing a translation process for your localization project.  

Let the driving objective in the endeavor be, of course, to place your team in the best position to get content translations finished and delivered to the people who need them as fast and effectively as possible.  

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14. FAQ About Choosing Machine Translation Software

There is a lot of information to consider when choosing the best Machine Translation engine for your business. Here are some frequently asked questions about MT and answers to help you make the most informed decision possible about all the factors involved in selecting the right MT option for your needs:  

What should I consider when choosing a corporate machine translation service? 

To identify the best machine translation software for your company, consider these factors, among others:  

  • MT Software Type  
  • Your Content Type  
  • Your Industry Type 
  • Desired Language Pairings 
  • Your Data Volume 
  • MT Security Types 
  • Compatibility with your TMS 
  • Budget 

Remember, although no MT software is designed especially for ideal processing of a particular content type, today’s best MT engines are trainable for specific data types and subjects.  

What types of machine translation software are there? 

Machine translation software is available in an array of MT designs, including AI-enhanced:  

  • Rule-Based Machine Translation (RBMT) 
  • Statistical Machine Translation (SMT) 
  • Neural Machine Translation (NMT) 
  • Hybrids of two or more of the above MT types  

What are the advantages of machine translation? 

For high-volume translation projects, MT enables you to move your content to your targeted recipients quickly, which can increase your ROI in sales marketing campaigns, promotional drives, branding initiatives, product roll-outs, internal marketing, policy adoption, safety training, other training, and virtually countless other localization goals. 

Which machine translation engines produce the best quality? 

The numerous free, online, ready-to-use MT engines are available for translations of multiple languages in many domains. But, integrated, custom MT engines provide incomparably greater functionality and flexibility. With whichever MT software type(s) you select, for best quality output, be sure to use human post-editing

Which machine translation software applications are GDPR compliant? 

Prioritize Data Security Rules: The relatively sophisticated Language Studio TM engine is GDPR compliant. But, not all MT engines are compliant. Generally speaking, on-premises MT systems are safest, secure cloud platforms are normally safe, and free online MT engines are the least secure MT engines and the least likely to be GDPR compliant. 

Are free web MT engines (such as Google) good for corporate use? 

In-house corporate language translation operations are advised to use machine translation software solutions that integrate multiple MT engines, including free online options like Google Translate, DeepL Translate, Amazon Translate, Bing Translator, and others.  

By integrating multiple machine translation software programs, TM processes can capture the tech resource that works best for given business purposes, content types, language pairings, and compliance needs. 

Which type of content is best handled by machine translation? 

Automated translation is ideal for processing large volumes of content at high speeds. The technology performs best with an abundant termbase to ensure consistent application of terms across the source content. MT is most reliably accurate in translating content largely consisting of commonly used words and expressions. MT software cannot comprehend cultural or contextual nuances, so MT is least well suited to translating heavily nuanced or contextualized content.  

Keep in mind that with any type of content, MT cannot be expected to render consistently precise matches with the original text. Machine translation quality is also less for source or target content in some languages than others, based on development levels in termbases, glossaries, customizations, and integrations. MT is most effective with any content type when utilized as a component of a larger translation process workflow that includes human post-editing

Can content be translated by MT without needing any human editing? 

The role of MT software in business translation projects is primarily to aid professional translators, speeding up the raw translation process to lightning speed, improving the overall accuracy of the total process, and slashing costs. 

Although in the vast majority of machine translation projects, post-editing is necessary, there are instances when only light post-editing, or even no post-editing is needed for MT. The need for post-editing of machine translations depends on:  

  1. The quality of the MT output. 
  1. Availability and extensiveness of the termbase. 
  1. How well users understand what is realistic to expect from MT.   

How is the quality of machine translation controlled? 

The accuracy of machine translation largely depends on the MT engine you use and the extent of training it has received for your purposes. The most useful measure of MT output quality is sentence-by-sentence scoring by human evaluators. Automatic evaluation methods are also applied to gauge consistency between machine and human translations. Basic control metrics include, for example: 

  • Word Error Rate (WER) 
  • Position-Independent Error Rate (PER) 
  • Rank-based Intuitive Bilingual Evaluation Scores (RIBES) 
  • Bilingual Evaluation Understudy (BLEU) 
  • Metric for Evaluation of Translation with Explicit Ordering (METEOR) 

Some popular QA testing tools for MT include Xbench, ServiceNow, Verifika, and others. The best CAT platforms also feature applicable QA tools.  

LSP QA testing experts further utilize an array of specialized localization QA processes.   

How many machine translation solutions should I incorporate into my tech stack? 

In-house corporate language translation operations are advised to choose MT solutions that enable integration of a comprehensive mix of MT engines. A customizable MT software solution for corporate projects, like Language Studio, for example, integrates a variety of CAT-compatible ready-to-use MT engines.  

Integrating a number of machine translation software options enables you to auto-capture those that work best for various languages, purposes, content types, and compliance needs. For example, Andovar’s customized Language Studio solution features integrated MT engines such as: 

  • Google (Cloud Translate API) 
  • DeepL Translate 
  • Amazon Translate (AWS) 
  • Other general-purpose MT engines 

Some other ready-to-use MT engines you may want to integrate into your platform include: 

  • Microsoft Translator 
  • Systrans 
  • Bing Translator 
  • IBM Watson Language Translator 
  • PONS Online Translator 
  • PROMT Translator 
  • Hybrids 

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15. Andovar Takes Your Content Global

We are a global media-focused content localization services provider. We have built our brand on customizing solutions for complex localization projects that have facilitated seamless global growth for our clients. Our experts in MT and localization systems can help you develop and implement the ideal translation and testing hardware system and the best machine translation software for your needs. Our language technology tools accommodate the largest-scale localization needs and make your life much easier as you expand your international market reach. 

Our expanding multi-cultural team currently includes localization project management experts, technical experts, and over 3,100 professional translators. We have helped eCommerce companies, games developers, software and technology companies and enterprise companies in various other industries deliver ideally localized content of all types throughout their global markets.  

Our international headquarters is located in Singapore, and we offer production and sales at our locations in China, Thailand, India, Hungary, the U.S., and Colombia.  

Call Andovar Pte Ltd., Singapore at +65 3159-3958, or contact us online, if you have questions about translation technologies or want to see how we can help you with your next localization projects

Contact Andovar

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