How machine translation software has evolved over the years

Machine translation development has come a long way, evolving through different phases, each with its own ups and downs. Let’s explore the various types of MT technology—including rule-based, statistical, and neural machine translation—in this overview.

Rule-based machine translation tools

The earliest form of machine translation developed in the 1960s, rule-based MT (RBMT), relied on a large, predefined set of linguistic rules that helped the software transfer the meaning of a text between languages. It had overall low quality, and it required adding language rules manually, requiring a significant amount of machine translation post-editing.

Statistical machine translation engines

Statistical MT (SMT), pioneered in the 1990s, builds a statistical model of the relationships between words, phrases, and sentences in a given text. It applies the model to a second language to convert those elements to the new language. Thereby, it improves on rule-based MT but shares many of the same issues.

Neural machine translation systems

Ever since Google brought about the “MT neural revolution” in 2016, machine translation output has significantly improved in quality, consistency, and productivity gains. Thanks to neural machine translation (NMT), algorithms are now able to generate such accurate translations that they are beyond just “good enough,” in many cases reaching human parity in translation.

Neural machine translation software employs artificial neural networks to process and translate text by learning from extensive multilingual datasets. Most of today’s MT software providers rely on neural MT since it offers more nuanced translations and the flexibility to add new language pairs, enabling them to learn and improve with increased usage.

Why use machine translation software?

 The introduction of modern machine translation software based on neural learning has elevated MT to an essential asset in business translation.

Let’s take a closer look at the 3 core benefits of machine translation software:

• Greater speed and volume
• Enhanced accessibility
• Reduced costs

Greater speed and volume

With ongoing improvements in machine learning algorithms and hardware, machine translation software is becoming even faster and more efficient. Not only is it capable of translating millions of words almost instantaneously, but it’s also continually improving as more content is translated.

For high-volume projects, MT software not only handles volume at speed, but it can also integrate with other software platforms such as content or translation management systems to organize that content. This helps maintain the structure and meaning of content when translating it into multiple languages.

Enhanced accessibility

Most machine translation applications offer support for up to 100 languages—and some of them even more. This means that translations can be simultaneously delivered to multiple target markets, creating a win-win situation for both businesses and customers.

By breaking down language barriers and enhancing the customer experience, MT has made content, products, and services more accessible to potential buyers around the world. This expanded reach also allows businesses to grow their market share and boost their profits.

Reduced costs

The combination of high-speed throughput and the ability to select from existing language pairs covering dozens of combinations means that machine translation software can reduce translation costs, even when human translators may still be post-editing.

Put simply, MT software starts the translation process by providing the initial output—human translators then come in to refine the translations, ensuring they capture the original content’s meaning and adapt it for the target audience in a specific region.

What is the best machine translation software?

Given the innovation potential in neural machine translation, global tech giants have been increasingly disrupting the language industry over the years, prompting an important question: What is the best machine translation software on the market, and what sets it apart from the rest?

As much as the MT software landscape evolves rapidly, there’s no one-size-fits-all answer: The best MT software varies based on your specific translation requirements. Even the widely held belief that NMT systems deliver high-quality translations has its own intricacies and limitations.

Key factors to consider when evaluating machine translation software
Capabilities	Different tools may excel in specific domains, so choose one that aligns with your translation needs (e.g., medical instructions vs marketing collateral).
Ease of use	Ensure the MT software has a user-friendly interface and is easy to integrate into your existing translation workflow.
Cost	Consider the pricing model, whether it’s free, subscription-based, or per-word.
Support	Evaluate the level of customer support offered, which can be crucial when issues arise while integrating or using the software.
Privacy	Examine the provider’s privacy policy and check its implications for your business with your legal department—to safeguard against potential data loss or privacy issues.

Google Translate

There’s hardly anyone who hasn’t heard of it: Google Translate has been integrated into nearly every product in the Google ecosystem and has achieved high-quality results—not only for the most common language pairs but also for less popular ones.

Google Translate was launched in 2006, but it was only a decade later that Google made one of the most significant leaps in recent commercial MT history by transitioning from a statistical, phrase-based system to a neural machine translation system. This marked the birth of Google Neural Machine Translation (GNMT).

In fact, it was a double leap. The first part involved abandoning the statistical paradigm, which was still tied to word-for-word translation and lacked context. Recurrent neural networks or RNNs (a specific form of neural networks used to encode and decode sentences) significantly improved their ability to understand context, resulting in much more fluent translations.

While other machine translation processes were already utilizing artificial neural networks, the real innovation lay in the introduction of the so-called “attention mechanism” or Transformer. This approach enables the system to translate lengthy sentences without sacrificing context and quality. Unlike RNNs, which gather information from each part of a sentence and can be computationally expensive, an attention layer can focus on the most critical aspects of the source text, regardless of sentence length.

At present, the free version of Google Translate allows you to translate text into over 100 languages by either typing or copy-pasting the text into the web interface. You can also translate entire documents by using drag-and-drop functionality and view the history of recently translated sentences, all without requiring a login or account.

It’s also possible to translate up to 500K characters per month using Google’s powerful Cloud API for importing and exporting translations, e.g., from and into computer-assisted translation tools. More characters and features are available in the paid versions, which offer functions like creating user-defined translation models using Google AutoML. In other words, every user can train Google’s engine with their own bilingual data.

DeepL

The wunderkind of machine translation software, DeepL, was launched in 2017 to further develop Linguee, the world’s largest database of human translations. The engineers at the German-based company applied the latest deep-learning technique (hence the company’s name) to get state-of-the-art machine translation software by training the models on Linguee’s data.

The results are very similar (or better, depending on language pair, field, and evaluation system) than the ones from Google Translate—which is still often considered the benchmark for translation quality—DeepL itself claims to be “the world’s most accurate translator.”

DeepL Translator offers a free machine translation software tool for seamless integration into the whole Windows/iOS ecosystem. Users can translate text to and from a total of 33 languages (as of 2024), by clicking CTRL+C twice—or translate entire Word and PowerPoint documents per drag-and-drop.

Also, both in the web interface and the standalone app, you can customize translations by “forcing” terminology for selected language pairs. Already in the free version, users can decide to use specific (corporate) terminology in their translation for certain language pairs by just switching a toggle. This way, DeepL tackles one of the weaknesses of machine translation we mentioned before—accuracy.

A wider, unlimited terminology function is available in the DeepL Pro (paid) version, along with other settings, such as the option to make the target text more formal or informal—something that can have a massive impact on the tone of voice in languages like German or Italian.

Remarkably, DeepL is one of the few top-notch MT providers not currently offering the option to customize the translation model by using proprietary, pre-translated data to train the engine.

On the other hand, DeepL Pro offers subscribers a CAT tool integration as well as end-to-end encryption of their translated data.

SYSTRAN Translate

SYSTRAN is the first company to offer machine translation for commercial purposes. Founded in 1968, it continues introducing interesting innovations—the latest being pure neural machine translation (PNMT).

Its free machine translation tool, SYSTRAN translate, allows users to translate text “on the go” into 140+ language combinations while trusting the power of the open-source community. “Open source” means freedom with the source code, allowing for more creative freedom and enabling technology agility.

Among the big players in the MT field, SYSTRAN is the only one sharing its knowledge—and profiting from cooperating—with OpenNMT, an open-source ecosystem for neural machine translation and neural sequence learning. This same engine, founded by SYSTRAN in December 2016, together with Harvard NLP, has already been successfully used in many research and corporate applications.

Paid subscribers can leverage this technology while also taking advantage of SYSTRAN’s PNMT to customize their MT models with their own bilingual data.

SYSTRAN is also one of the few companies that don’t just speak about NMT for each aspect of the translation cycle. It defines its own model as hybrid translation, combining rule-based and statistical machine translation to guarantee:

• Predictable and consistent translations
• Compliance with corporate terminology
• Out-of-domain usability
• High performance
• Learning from existing mono and multilingual corpora with minimal customization costs, etc.

Microsoft Translator

Much like Google Translate, Microsoft Translator is integrated into Microsoft’s own search engine, Bing. Moreover, many Microsoft products now include the possibility to translate documents (Office), messages (MS Teams, Skype), or posts (LinkedIn) between 90 languages and dialects using their home-brewed MT system—also based on the newest neural network technology with an attention-based model.

Microsoft officially launched Translator in 2009, but the product gained more relevance and was updated only in the last few years. Today, it’s also available as a standalone mobile app for both iPhones and Android devices.

An interesting feature in Microsoft Translator’s free version is the translation of frequently used expressions relevant to daily life. Users can quickly translate commonplace questions and statements for conversations with a single click. The app also supports the translation of street signs through image recognition.

For those seeking to integrate Microsoft’s translation technology into their translation workflows through a paid subscription, the offerings are extensive. Besides the “standard” text translation, with Azure Cognitive Services Translator, it’s possible to translate entire documents, utilize a REST API, and leverage AI Containers. Most importantly, it allows for the machine translation customization.

According to Microsoft itself, with about 10K bilingual translated sentences (input and output), it’s possible to improve the translation models to fit the customer’s needs better.

Amazon Translate

Last but not least, Amazon Translate, another online machine translation system from a Big Tech company—relatively young, having been launched in 2017, yet very powerful. Given the backing of its parent company, it’s not surprising that Amazon Translate has achieved remarkable results in the short time since its release. Users looking for a free solution might be disappointed, though: To use Amazon’s machine translation engine, you need an AWS account.

With an AWS account, you get two million free characters per month—and the range of functionalities offered by this NMT system is huge:

• Customization (terminology and parallel data)
• Encoding terminology
• Batch translation (Amazon S3), etc.

What about ChatGPT?

We would be remiss not to mention OpenAI’s ChatGPT in the conversation about the best solutions for automated translation, especially given how much attention generative AI has been attracting since its arrival in late 2022.

 

While not necessarily built with translation in mind, GPT-like applications can easily perform language-related tasks and are a viable and attractive alternative to the more established MT solutions mentioned above.

In fact, GPT-4o (and other large language model-based solutions such as Claude 3 Opus, PaLM2 Text Unicorn, or Google’s own Gemini Pro 1.5, among others) perform as well as MT solutions. In some combinations of domain and language pairs, they can possibly even outperform MT. Today, LLM-enabled translation is quickly becoming a part of the language services landscape.

As in the previously mentioned cases, GPT-like applications come with their pros and cons, which businesses need to carefully weigh when considering automated translation solutions:

• LLM-based automated translation is cheaper than traditional MT, 
• Yet it is also significantly slower than traditional MT, making it less suitable for scenarios where real-time translation capabilities are needed
• Same as MT, LLMs do offer potential for customization through the use of techniques such as retrieval-augmented generation (RAG) or prompt engineering

Choosing the right machine translation tool for your needs

This overview shows that even though almost all machine translation developers have moved towards NMT, each provider has gone in its own way. On one hand, there’s the strategy of reaching a broader audience with a completely free, user-friendly tool. On the other hand, some providers offer more customization options geared towards tailoring the tool to your specific needs.

Recognizing that each provider has its strengths and weaknesses, it’s clear that there’s no single best machine translation software—nor a one-size-fits-all solution.

This will help you find the right combination of MT engines to meet your specific translation needs. However, you can unlock the full potential of machine translation only when you’re able to access and use all tools in one place. Thanks to a cloud-based translation management system, this is now well within your grasp.

Make the most of MT software with Phrase TMS

When businesses need to utilize machine translation at scale, they need technology that can provide them with the best of both worlds: efficiency and quality.

Phrase TMS, the enterprise-ready translation management system within the Phrase Localization Platform, makes it effortless for growing companies to leverage machine translation. Organizations achieve an unprecedented ability to enter new markets more quickly and efficiently.

Phrase TMS users can employ a dedicated machine translation management tool, Phrase Language AI, to implement MT into their translation workflow with fast and cost-effective translations that don’t compromise on quality.

Fully embedded into Phrase TMS, the advanced MT management capabilities that come with Phrase Language AI let you:

• Start translating immediately with no developer time or effort using fully managed MT engines from leading providers like Google, Amazon, DeepL, or Microsoft.
• Add any of the 30+ supported generic and custom engines manually if you ever prefer to use a specific MT engine.
• Extend high-quality MT to every employee: The powerful Phrase Language AI API allows you to scale the value of MT with company-wide access for to secure company-approved machine translation.
• Enjoy unlimited machine translation for post-editing workflows so linguists can work more efficiently.
• Work with the best engine—auto-selected, based on your language pair and content type.
• Automatically filter out content that shouldn’t be machine-translated.
• Delegate quality testing, legal and security evaluation, setup, and payment of machine translation engines to dedicated machine translation experts on the Phrase team.
• Leverage your translation memories to increase translation quality by up to 50% with Phrase NextMT—the first TMS-ready MT engine.
• Ensure the MT engines use your preferred terminology with the correct morphological inflection—reducing post-editing effort.
• Preserve formatting and placeholder tags from source to target content automatically.
• Get a score for each machine-translated segment, based on past performance data, to post-edit only where needed.
• Achieve up to 55% cost savings with MTPE compared to human translation.

Discover advanced MT management features with the Phrase Localization Platform and push MT technology to the next level with our enterprise-ready solution.

The building blocks of a translation workflow

A translation workflow is a structured sequence of steps designed for the efficient execution of tasks involved in transferring the meaning of text between languages.

Translation workflows typically vary from one business to another, yet they all share a common goal: To deliver high-quality translated content that has the power to resonate with international audiences—all while saving you time and effort.

An effective translation workflow seamlessly connects the various stages of translation to meet your specific business needs, regardless of the nature of the products, services, and industries it’s embedded in:

• Preparation: Gathering and evaluating source materials, deciding on human or machine translation, and uploading content with references.
• Translation: Adapting source content to the target language, considering context and cultural nuances, and post-editing MT output.
• Editing and proofreading: Reviewing translated content for grammar, terminology, and brand alignment.
• Quality assurance (QA): Ensuring translated content works in the intended context, performing layout and formatting checks.
• Delivery: Exporting translated content in the desired file format and integrating it for a seamless user experience and accessibility.

Why traditional translation workflows impede growth

To truly appreciate the power of automated web translation workflows, let’s rewind a bit. Not too long ago, spreadsheets were the norm for LSPs translating texts from one language into another. The result would often be translated content filled with errors, duplicates, or omissions.

With advancements in computer technology, translation workflows have become increasingly automated. Computer-assisted translation tools, such as translation memory and term bases, have removed the need for creating and managing multilingual content manually.

As the global market becomes more connected and translation requirements evolve, cloud-based translation management systems (TMSs) now go a step further by enabling businesses to own, control, and orchestrate entire translation workflows within their digital ecosystems.

For businesses embarking on a global expansion journey, establishing an effective translation workflow can be a demanding mission. One of the key obstacles is the limited customization options offered by most translation management solutions on the market.

As a result, companies often find themselves settling for a one-size-fits-all automation approach, resorting to workarounds or external support. This, in turn, requires more time, increases costs, and limits scalability—ultimately impeding growth. Let’s explore each of these limitations further.

The toll of traditional translation workflows
Time-intensive	Translation workflows involve several steps, each taking its fair share of time. Additionally, back-and-forth communication between language service providers (LSPs) on the vendor side and buyers can extend project timelines, and error corrections delay translated content delivery.
Costly	Translation workflows can be costly due to manual, labor-intensive tasks, translator fees per word, and extensive quality checks. When executed manually, they can impose a considerable financial burden on businesses seeking translation services.
Limited scalability	In traditional translation workflows, buyers often depend on LSPs. That’s why expanding content translation efforts is more likely to succeed if the LSP is capable of involving more translators or embracing new technology to accommodate the evolving translation demands.

With this in mind, an effective translation workflow should ideally function as a finely tuned instrument customized to your business needs. It should seamlessly integrate into the product development process, aiming to provide timely, high-quality translations while also saving you time and effort.

This is where Phrase Orchestrator comes into play.

Building effective translation workflows with Phrase Orchestrator

Phrase Orchestrator is a no-code, drag-and-drop workflow automation solution that lets you use the full functionality of the all tools across the Phrase Localization Platform: From the enterprise-ready translation management system, Phrase TMS, to the dedicated software localization platform, Phrase Strings, and more.

Phrase Orchestrator is designed to give teams seeking advanced translation workflows control and increased time savings through 2 core functionalities—the workflow editor and management experience.

Workflow editor

In the workflow editor, you can create both simple and complex workflows with triggers, actions, and conditions. Every workflow starts with a trigger, i.e., a specific event in the Phrase Localization Platform (e.g., “File uploaded to a Phrase TMS project”), to which users can add any number of actions that contain some functionality, like “Create a Phrase Strings project,” to run as a series or in parallel.

Actions and triggers can have conditions, like logic gates, to determine if and when they should run (e.g., “Only start a job if it contains more than 9 keys” or “Run this workflow every Tuesday at 1 p.m.”). Furthermore, actions can be configured in both sequential and concurrent sequences, allowing for branching workflows. Once the workflow is finalized, the user can publish it, and it will be automatically activated whenever the specified trigger event or time occurs.

To get workflows up and running more quickly, users can also apply templates from the curated template library, designed to address the most prevalent use cases and scenarios.  

Management experience

Management experience enables users to manage, organize, and monitor their workflows. Users can create folders to keep their workflows organized, create multiple versions of a workflow, edit them, and switch between them. Additionally, users can monitor functionality to keep track of workflow performance and errors, inspect every single execution of their workflow, and see if and which problems occurred.

5 common use cases for Phrase Orchestrator

To get a better understanding of how our cutting-edge workflow automation solution can empower you to achieve the best possible results and become even more efficient, let’s have a look at the most common use cases for Phrase Orchestrator.

Dynamically export content to an online repository
When the status of a job changes to “Completed,” an automated process is triggered to export the content to an online repository.	Automate processes, reduce manual intervention Cut time by 90%, enhance efficiency and accuracy Offer automatic status updates for progress tracking Streamline workflow for fast, cost-effective localized content delivery

Dynamic workflow based on automated QA detection
Workflow automation triggers a review step only when a quality assurance check flags potential issues. QA checks can be customized according to project requirements.	Incorporate automated QA for error-checking and improve translation quality Automatic reviews due to QA results save up to 84% of time and resources, enhancing quality and consistency Standardized QA checks promote uniform translations for cohesive projects for progress tracking

Automated MT quality assessment
Use machine translation (MT) workflows to handle bulk content with limited human intervention while maintaining quality. Trigger post-processing workflows for quality checks, post-edits, formatting, and more.	Scale MT workflow processes for large content volumes Run jobs directly with machine translation, focusing post-edit efforts where needed Reduce time spent by 30-60%, depending on content and MT output quality

Automate the process of pivoting languages
Automate the process of using a third language as a bridge language to fill the gap between 2 less common languages e.g. Polish › English › Thai.	Combine two processes in one workflow Eliminate idle time Eliminate manual, repetitive, and time-consuming tasks Make the process 5x faster

Automatically pre-translate content using pseudo-translation
Test the localization process and UI prior to translation, enabling early detection of issues with character encoding, layout, or display during development.	Identify localization issues early, saving valuable resources Enhance product quality by pre-translation issue addressing Reduce time to market

Automating translation workflows is easier than ever before

By taking workflow automation to new heights and providing invaluable flexibility, Phrase Orchestrator enables you to build custom workflows perfectly tailored to your translation requirements.

With an express workflow setup, it eliminates the need for dedicated engineering support, while its plug-and-play interface delivers a seamless user experience.

Explore its full potential with a 30-day free trial, and see for yourself how it can provide you with unparalleled speed and control.

How Google Translate’s accuracy improved over time

Google Translate has been a key player in MT technology development, steadily improving in accuracy since its launch in 2006. In its early days, it followed rule-based statistical MT methodology, using a set of existing translations (corpora) to translate specific words within sentences.

The quality of statistical machine translation was fairly basic, and developers had to manually define and program what effectively constituted a large set of rules. Due to the low translation quality and high development cost, Google came up with a new MT model in 2016: neural machine translation (NMT).

The rise of neural machine translation

Instead of running a set of predefined rules from the start and inspired by the way human brains work, Google Neural Machine Translation (GNMT) is using neural networks to handle complete sentences as examples of inputs (source text) and outputs (translated text) to predict the translation result.

NMT understands context, can be customized quickly, and detects long-distance dependencies to improve the interpretation of meaning. Its capability to generate translations that resemble human output has made NMT today’s industry standard—with companies like German-based DeepL also competing in the market.

Google Translate’s Transformer model

In 2018, Google Translate released its Transformer model, which introduced attention mechanisms that revolutionized the accuracy of machine translation. By using self-attention layers to better capture context, the model was able to understand and generate translations more accurately than ever before.

Apart from computational performance improvements, the Transformer model has enabled Google Translate to better contextualize words and phrases in a sentence by providing insights into how the neural network “sees” them. It works with a variety of language pairs, and its accuracy varies accordingly.

Google Translate accuracy overview

A 2011 research study found that Google Translate worked well for many European languages but not as accurate for some languages spoken in Asia. The top 10 languages for translation accuracy with English in Google Translate were (from best to worst):

• German
• Afrikaans
• Portuguese
• Spanish
• Danish
• Greek
• Polish
• Hungarian
• Finnish
• Chinese

In 2019, a reevaluation using the same input as the original study showed a 34% improvement in Google Translate’s accuracy.

A more recent study, carried out in 2021 by the UCLA Medical Center, revealed that Google Translate maintained the general meaning in 82.5% of the translations. However, the accuracy levels ranged from 55% to 94%. Here’s a brief overview of the results:

Accuracy levels of Google Translate for different target languages from English source content
Spanish	94% accurate
Korean	82.5% accurate
Mandarin Chinese	81.7% accurate
Farsi	67.5% accurate
Armenian	55% accurate

In general, Google Translate—and most machine translation (MT) systems—excel in languages with abundant training data and high market demand. This typically prioritizes language pairs involving English either as the source or target language, often favoring European languages.

Asian languages, on the other hand, tend to pose challenges because of limited available training data and unique linguistic complexities less typical for European languages. That being said, the performance of MT engines continually undergoes retraining and improvement—leading to changing levels of accuracy and fluency.

How do you assess Google Translate’s performance?

There’s a variety of evaluation metrics for assessing the quality of machine translation engines like Google Translate, with BLEU (BiLingual Evaluation Understudy) and TER (Translation Error Rate) being most widely used.

However, those aren’t the only metrics one can use to evaluate Google Translate’s performance. At Phrase, for example, we have our own proprietary evaluations of how Google Translate and other MT systems perform in terms of accuracy and quality—and we carry them out once a quarter:

In our quarterly Machine Translation Report, we bring together the latest performance data for all of the major MT engines used in real workflows in Phrase TMS, the enterprise-ready translation management system within our Phrase Localization Platform.

We consider 2 key criteria from the start: language pair (source vs target language) and content type (domain).

According to the latest MT Report, the top 3 language pairs used in machine translation projects in Phrase TMS are:

• English-Spanish
• English-French
• English-German

When it comes to content type per language pair, Google Translate has achieved the highest performance scores in the following domains:

• Medical for English-German (91.62)
• Industrial for English-Spanish (86.01)
• Entertainment for English-German (84.34)

The result is based on anonymized machine translation post-editing data collected in a period of 6 months. To gather precise MT quality results, we’ve filtered translation segments to reflect the required post-editing effort as closely as possible: Either MT was used and post-edited, or the linguist translated from scratch despite the availability of MT—suggesting that the MT quality was too low for post-editing.

The result is based on anonymized machine translation post-editing data collected in a period of 6 months. To gather precise MT quality results, we’ve filtered translation segments to reflect the required post-editing effort as closely as possible: Either MT was used and post-edited, or the linguist translated from scratch despite the availability of MT—suggesting that the MT quality was too low for post-editing.

Can you trust Google Translate?

As language and translation are both dynamic categories that intrinsically reflect processes—not static phenomena—accuracy should also be seen as a relative concept. Accuracy in translation will depend on the original intention of the author and the destination of the message. For example, expectations of accuracy in grammar, style, and register for an email will greatly vary from the expectations of accuracy for a novel.

A frequently heard opinion is that Google Translate’s free MT service is accurate enough for most users because they need to translate simple messages—and what matters most is that the audience is able to grasp the sense of it rather than the complete “native” message. It can then be considered accurate enough because expectations are low.

As a rule of thumb, the accuracy of Google Translate’s free MT tool is less consistent when:

• Used as a dictionary to translate single words: Google Translate struggles to produce an accurate result, i.e., as intended by the author, because of the many meanings a single word can have; this is true for English as well as for other widely spoken languages
• Translating familiar expressions that don’t have a direct equivalent in the target language
• Non-verbal expressions are an important part of the message, e.g., when being ironic
• Grammatical rules aren’t properly used in the source language or used differently in the target language, such as the subjunctive mood in English

For business purposes, when a large amount of content needs translation across domains, Google offers its Cloud Translation connected system. Companies can either set it up themselves or rely on a translation management system (TMS) to fully manage it from day one. Cloud Translation offers customization features for domain and context-specific terms as well as the possibility to train custom translation models.

Google’s Cloud Translation offering makes an official statement that it doesn’t use any content submitted for translation for any purpose other than providing the translation service. Nevertheless, it’s unclear how the company uses the information submitted to the free version of Google Translate—or if the data influences business decisions in any way.

Does Google Translate have any major competitors?

While Google Translate may be the first name that will pop up when discussing machine translation, there are several competing machine translation software vendors on the market—each of them offering a specific approach to MT.

Here are some of Google Translate’s major competitors to consider when looking for the best machine translation engine:

Amazon Translate

Amazon Translate is part of Amazon Web Service, a subsidiary of Amazon, providing on-demand cloud computing platforms and APIs for both individuals and businesses. It’s based on NMT technology as well.

Amazon Translate supports translation between 75 languages.

DeepL

DeepL is a German-based online MT service that was launched in 2017. It uses a proprietary algorithm with NMT technology and can process DOCX, PPTX, and PDF files while retaining footnotes, formatting, and embedded images.

DeepL supports 26 languages, forming 650 target-to-source combinations.

Systran Translate

Systran is a translation technology company founded in 1968 by a researcher at the California Institute of Technology. It’s one of the first companies to start developing MT software. Its original objective was to improve the translation of Russian into English.

Starting with rule-based MT technology, it developed hybrid RbMT/SMT technology and has since then switched to NMT.

With Systran Translate, you can translate into 50 languages.

Microsoft Translator

Microsoft Translator is a multilingual MT cloud service provided by Microsoft. As part of Microsoft Cognitive Services, it’s integrated with multiple consumer, developer, and enterprise products.

Microsoft Translator supports over 100 languages.

Tencent

Tencent Machine Translation is the main MT offering by Chinese technology giant Tencent. The solution combines both NMT and SMT models.

Tencent Machine Translation supports over 160 different language pairs.

Will Google Translate ever be perfect?

Translation isn’t just about converting words from one language into another. If it were so, a dictionary would be the only necessary tool of the trade, and we all have seen the very poor (and sometimes very funny) results of working that way. This is because a message isn’t only made of words—it also contains context, intention, non-verbal aspects, etc.

That said, Google Translate has been rapidly advancing over the years, but it still can’t do much that human translators can:

• Ask questions
• Understand context
• Catch irony
• Translate creatively
• Make considered choices
• Do research
• Observe consistency
• Guarantee completeness
• Deliberately leave out or include information
• Add glosses/notes

No one knows if and when technology can reach the human level of semantic acuteness, but that’s exactly the goal for many. Quantum computing, for example, aims to increase the number of operations and data that can be processed, so one day it may be able to learn without human interaction and get a better understanding of the creation of language.

How to use Google Translate: Best practices

Google Translate has grown into a strong productivity tool that can save you time and spare you the hassle of looking for a good translator. Generally speaking, you can use Google Translate for texts that don’t need to be perfect in terms of style and consistency, i.e. for anything that won’t make or break your brand:

• Low-visibility or low-traffic content, such as internal documentation, website footers, social media posts for sentiment analysis, etc.
• Repetitive technical content that only needs to be actionable, like instruction manuals, for end-users to access key information to solve a problem
• User-generated content like product reviews, for which consumers generally don’t expect high quality
• Quickly perishable content, like chat or email support messages, customer enquiries, etc.
• Large bulks of content with a short turn-around, such as hundreds of product descriptions that need to go live quickly
• Frequently amended content like feature and information updates

Nevertheless, if you decide to rely exclusively on Google Translate, you may run a considerable risk of your translation lacking important information, meaning, or grammar. To avoid those pitfalls, it’s key to review and adjust your MT output. This process is known as machine translation post-editing (MTPE).

Depending on the level of accuracy you want to achieve, you can apply light or full post-editing. Both approaches will give you the benefits of using MT output while ensuring that your message reaches the intended goal from the start.

As a general guideline, the below cases require machine translation post-editing:

• Product titles: They are highly informative and concise, they tend to contain proper names and polysemous words, and their word order is usually relatively free, which can cause ambiguity.
• Translations between language pairs of dissimilar syntax, like Japanese and Spanish, because the reordering of words and phrases to well-formed sentences becomes more challenging for machine translation engines.
• Product descriptions: They need to be well-crafted and clearly state the product’s features or benefits without room for ambiguity.
• Content of medium visibility that needs to be as accurate as possible: knowledge base, FAQs, alerts, etc.
• Back-end SEO meta information such as image alt texts and captions: While their visibility is low, a human needs to ensure that the target-language keywords are present.

All in all, like all other free MT services, Google Translate’s free MT tool is quite handy when you want to translate relatively simple pieces of text quickly. However, for an accurate translation that properly conveys the original meaning, you’ll want to consider post-editing as the most effective way to use machine translation in the long run.

Why is market positioning important?

Without effective market positioning, you run the risk of leaving your brand and product reputation to chance. At best, consumer beliefs and images of your offerings may fail to reflect your goals. At worst, harm to your brand impacts your profitability. That’s why it’s essential to get market positioning right the first time. Let’s have a look at some of its core benefits. 

• Market positioning allows businesses to establish a unique identity in the market: When you differentiate yourself from competitors, you can attract customers who seek specific benefits or solutions.
• It also impacts consumer perceptions and purchasing decisions: When a product is positioned effectively, your customers are more likely to perceive it as superior and willingly pay more.
• Finally, market positioning helps businesses build long-term customer loyalty: By consistently delivering on their promises, companies can develop a strong brand reputation and create a loyal customer base.

Types of market positioning

You have many positioning choices available to you depending on the nature of your product or brand. Each type of market positioning strategy caters to different customer needs and competitive landscapes. The key types of market positioning include:

• Product attributes positioning: This positioning type focuses on specific product attributes that differentiate it from competitors. For example, a smartphone company may position its product as having superior camera quality compared to other brands.
• Price positioning: Price positioning involves positioning a product as being affordable or targeting a specific price range. Companies may position their products as high-end and premium priced or as value-for-money options.
• Quality positioning: Quality positioning emphasizes the superior quality of a product compared to competitors. Companies may highlight factors such as durability, reliability, or exceptional craftsmanship to justify a higher price point.
• Usage positioning: This type of positioning focuses on the specific use or application of a product. For example, a laundry detergent may position itself as the best choice for removing tough stains.
• Competitor positioning: Competitor positioning involves directly comparing a product to competitors. This type of positioning is commonly used when a product offers clear advantages over competitors in terms of features, performance, or pricing.

Successful market positioning examples

To illustrate the concept of market positioning, let’s explore some real-life examples of successful marketing positioning:

• Apple: Apple has positioned itself as a brand synonymous with innovation, high quality, and user-friendly design (“Think Different”). Its products are known for their sleek aesthetics, intuitive interface, and seamless integration across devices.
• Volvo: Volvo has positioned itself as a brand synonymous with safety (“For Life”). Its vehicles are known for their advanced safety features and commitment to protecting drivers and passengers.
• Nike: Nike has positioned itself as a sports and fitness brand that inspires customers to push their limits and achieve greatness (“Just Do It”), emphasizing empowerment and motivation in its marketing campaigns.
• Airbnb: Airbnb’s market positioning (“Belong Anywhere”) revolves around providing unique travel experiences by offering accommodations that allow travelers to feel like locals and truly immerse themselves in the destinations they visit.
• Coca-Cola: Coca-Cola has positioned itself as the classic, timeless cola beverage, with its latest tagline (“Taste the Feeling”) highlighting the emotional connection that consumers have with Coca-Cola’s refreshing taste.

What is a market positioning strategy?

A market positioning strategy refers to the overall approach a business takes to position its product or brand in a target market. An effective market positioning strategy clearly defines the 4 Ps: product, price, place, and promotion.

With a market positioning strategy, you ensure that you understand customer needs and have both a unique selling point (USP) and a unique value proposition (UVP). It will also guide all promotion efforts and shapes how a company communicates with its target audiences. By consistently delivering the message and reinforcing the desired image, your business can build brand awareness and loyalty among consumers.

How to develop a market positioning strategy

Developing an effective positioning strategy requires careful planning and consideration for companies to create a strong market presence and gain a competitive edge.

By understanding the target audience, identifying the USP, analyzing the competition, and effectively communicating the desired image, companies can create a strong market presence and gain a competitive edge.

Here are some essential steps to creating a market positioning strategy:

• Conduct market research: Start by understanding your target market and its needs. Conduct extensive research to gain insights into customer preferences, competitor offerings, and market trends.
• Define your target market segment: Clearly define the specific market segment you aim to target. Consider factors such as demographics, psychographics, and purchasing behavior to create a detailed customer profile.
• Identify unique selling points: Determine what sets your product or brand apart from competitors. Identify the unique features or solutions that resonate with your target audience.
• Define a unique value proposition: Craft a clear statement that communicates the exclusive benefits and value your product offers, setting it apart from the competition.
• Create a consistent brand image: Ensure that all marketing efforts align with the desired positioning strategy. From brand messaging and visual identity to customer interactions, consistency is key in establishing a strong market presence.

That being said, market positioning is an ongoing process that requires continuous monitoring and adaptation. As consumer preferences and market dynamics change, you need to stay agile and adjust your positioning strategy accordingly. This may involve reevaluating the target audience, refining the USP, or even repositioning the brand entirely.

Measuring the impact of your market positioning strategy

Measuring the impact of market positioning is essential to evaluate the effectiveness of your strategy and make necessary adjustments. While the specific metrics may vary depending on the nature of your business, there here are some key indicators to consider.

Key metrics for assessing market positioning effectiveness
Market share	Monitor changes in market share to determine the extent to which your positioning strategy is attracting customers and driving growth.
Brand recognition	Measure brand awareness metrics, such as aided and unaided recall, to find out how well your market positioning has helped establish your brand in the minds of customers.
Customer perception	Conduct customer surveys or gather feedback to gauge how customers perceive your product. Assess whether it aligns with your desired positioning.
Sales performance	Analyze sales data to assess the impact of your positioning strategy on revenue generation. Look for trends or patterns that indicate success or areas for improvement.
Competitor analysis	Continuously monitor competitor positioning and compare it to your own. Identify areas where you may need to refine your strategy to maintain a competitive edge.

Market positioning is the key to global success

Market positioning is an integral component of any global expansion strategy. By effectively positioning your product or brand in different target markets, you can differentiate yourself from competitors, grow your customer base, and build long-term loyalty. 

Whether through product attributes, price, quality, usage, or competitor comparison, positioning strategies play a crucial role in shaping customer perceptions and influencing purchasing decisions.

Finally, continuously evaluating the impact of your positioning strategy and making adjustments as needed can help you stay ahead in a competitive market.