How to Choose the Best Machine Translation Engine in 2023
As businesses expand across borders, the demand for better, faster, and more dependable translation solutions becomes a priority. The number of machine translation engines available on the market today is large, making it difficult to choose the perfect option.
Understanding how to choose the best machine translation engine for your company is critical, as it can have a significant impact on your capacity to interact with your worldwide audience. This guide will break down the process, providing important insights and practical ideas to help you find the best translation option for your specific needs and goals.
Pro tip: Seamlessly integrate a machine translation engine into your localization workflow by using a professional translation management system – Centus. Learn more.
Here’s what you’ll learn:
When assessing machine translation, there are three main approaches: manual assessment, automatic assessment, and semi-automatic assessment. Let's explore each approach in detail.
1. Human assessment
Human assessment involves professional translators rating the quality of machine-generated translations. Translators assign overall quality scores, commonly on a scale of 1-10 or as a percentage. This evaluation relies on the skill and judgment of human translators who compare the machine-generated translation to a reference human translation of the identical source text.
In human evaluation, translators also judge whether a machine-translated word/phrase is the right fit based on the context. For instance:
"The cat is playing with a ball."
When machine-translated, it could potentially yield a poor fit with phrases like:
"The feline is engaging in recreational activities with a sphere."
In this example, the machine translation may replace "cat" with "feline" and "ball" with "sphere." This sentence is not exactly wrong, but definitely unnatural.
Shortcomings of human assessment: Human evaluation is subjective, inconsistent, and time-consuming. It relies on the availability and expertise of human evaluators, which may not be scalable.
2. Automatic assessment
Automatic evaluation uses computational measures to analyze the quality of MT output. Standard metrics are used to examine aspects such as fluency, adequacy, and lexical similarity. These metrics provide quantitative indicators of machine translation quality. Popular metrics include:
BLEU (Bilingual Evaluation Understudy) compares the MT output to high-quality reference translations using n-grams, which are segments of consecutive text. BLEU calculates a score based on the overlaps between n-grams, ranging from 0 to 1, with 1 indicating a perfect match with the reference translation.
METEOR (Metric for Evaluation of Translation with Explicit Ordering) measures how well the machine translation matches the reference translation by comparing the words used and their order. It calculates precision (how many words in the machine translation match the reference) and recall (how many words in the reference match the machine translation) to give an overall score.
TER (Translation Error Rate) counts the number of edits needed to change the machine translation into the reference translation. It includes insertions, deletions, and substitutions. The score is calculated by dividing the total number of edits by the total number of words in the reference translation.
NIST (from the National Institute of Standards and Technology) calculates the similarity between the machine translation and the reference translation by comparing sequences of words called n-grams. It assigns scores based on how many n-grams match between the two translations. The scores are combined to give an overall measure of similarity.
Shortcomings of automatic assessment: Automatic evaluation measures have limits, despite the fact that they provide an objective and efficient evaluation. They may not fully convey the nuances of translation quality, particularly regarding fluency and contextual accuracy. Metrics like BLEU are known to favor translations that are close to the reference, resulting in overly literal translations.
3. Semi-automatic assessment
Semi-automatic assessment incorporates both human and automated evaluation methods. It involves human translators reviewing and post-editing machine-generated translations. This is different from human assessment because humans are only involved as editors.
Shortcomings of semi-automatic assessment: This evaluation is time-consuming and resource-intensive. It requires machine translation post-editing, which increases the entire review timeline and cost. Furthermore, the quality of post-editing can vary, impacting the final assessment's accuracy.
By considering the strengths and limitations of each evaluation method, businesses can gain a comprehensive understanding of MT engine performance. Nevertheless, to produce a more precise and comprehensive review, it is advised to use a combination of evaluation techniques.
Choosing the right MT engine can be challenging due to the following factors:
1. Not all translation engines are created equal
The accuracy and fluency of translations can vary significantly from one engine to another. Some engines might be proficient at translating certain target languages but might falter with others.
This inconsistency is due to different levels of sophistication in their respective language models and the availability of high-quality training data for each language pair. All this makes the process of finding an engine with the right balance of accuracy and fluency across multiple target languages a daunting task.
2. Domain specialization
Industry-specific translations remain a challenge. From the dense legalese in contracts to the intricate jargon of medical reports, industry-specific content requires a specific depth of understanding and precision. General-purpose engines might struggle to effectively translate this specialized vocabulary and phraseology, leading to mistranslations that could have significant consequences.
3. Integration capabilities
Implementing new technology in your business workflow can be a complicated process. Seamless integration with your existing systems is essential to ensure minimal disruption to your operations. An MT engine might offer exceptional translation capabilities, but if it poses compatibility issues with your existing software, it can cause more problems than it solves.
As businesses grow, their translation needs are likely to increase. An engine that works well for a small volume of content might struggle as the workload scales up. Your chosen engine should handle increased translation volume without compromising on speed or quality. On the other hand, if an engine is too complex or powerful for a company's current needs, it might result in unnecessary costs too.
There are a variety of factors to consider and numerous engines to choose from. However, focusing on key aspects can simplify the process and guide you toward an optimal decision.
Content type and language pairs
Every translation task comes with its own unique requirements, depending on the content type and language pairs involved. Certain engines may excel at translating specific kinds of content — like legal documents or technical manuals — while others may perform better with different language pairs. For instance, English to Spanish might work splendidly, while English to Mandarin could prove more challenging. Evaluating the proficiency of an engine at handling your specific language and content needs is crucial.
The complexity and usability of the MT engine interface can impact the overall experience. An engine that's difficult to navigate or operate can slow down your workflow and make the process frustrating. Consider factors like setup, ease of use, and the availability of support resources when choosing an engine.
The quality of machine translation
The primary purpose of machine translation software is, of course, to translate and to do so accurately. Pay attention to the quality of translations produced by the engine. This involves evaluating its capacity to understand and accurately translate the context, idiomatic expressions, and cultural nuances, in addition to the literal meanings of words.
Generic translation engines, such as Google Translate, offer broad translation capabilities suitable for a wide range of applications. In contrast, customizable engines can be trained with specific data to better handle niche content types, industry jargon, or specific language pairs.
Make sure you evaluate what your translation work involves before investing in a generic or customized translation solution. Bespoke is not always the best option, and neither is a generic engine.
The cost of machine translation engines can vary due to differences in pricing models, additional features, and levels of service offered by each engine.
It's important to have a clear understanding of your translation process needs, budget constraints, and the pricing models of different engines. These could include charges per word, per minute, or by subscription. It's not just about the initial cost either. Consider any extra costs that may arise from the need for post-editing or additional support services. You might not need flashy and expensive add-ons.
Depending on your industry or the type of content to be translated, there may be legal requirements or standards that need to be met. For instance, in the medical and legal translation industries, there are often strict guidelines around the accuracy, authenticity, and confidentiality of translations.
Make sure the MT engine you choose is capable of meeting these requirements, maintaining the necessary level of precision and security.
Your translation engine should be able to merge with your existing systems and workflows.
Compatibility issues can cause disruptions, reduce efficiency, and increase costs. Therefore, it's crucial to ensure that the engine you choose can work well with your existing infrastructure, whether that's content management systems, translation project management tools, translation memory software, or other solutions used in your business.
Pro tip: Centus integrates with multiple MT engines, making your choice of translation solution easier. Learn more.
Maintenance and support
Consider the level of technical support offered by the provider of the MT engine. Do they have robust customer support capabilities?
Regular updates and prompt bug fixes are important to ensure the tool remains reliable and effective. Also, consider whether the provider offers responsive customer service to assist with any issues or queries that may arise. A provider that offers comprehensive support can save you time and help avoid potential disruptions.
Privacy and confidentiality
If you're translating sensitive or confidential content, the translation engine's privacy and data protection measures become critical. It's essential to ensure the engine you choose complies with relevant data protection regulations and offers strong security measures. This could include encryption of your data, strict access controls, and comprehensive data privacy policies.
Consistent tone of voice
Translating the literal meaning of the text and maintaining the original tone of voice in the translated content are equally crucial. This can have a significant impact on the effectiveness of your content and how it's perceived by the audience.
Some translation engines might be better equipped to handle this than others, employing advanced techniques to not just translate words, but also capture the sentiment and tone of the original text.
Glossary and memory support
Translation memory (TM) and glossaries can greatly improve the consistency and efficiency of translations. TMs store previously translated sentences, which can be reused in future translations, while glossaries define key terms to ensure they are translated consistently. Check if the translation engine supports the use of TMs and glossaries, as they can help you save time and maintain consistency across all your translations.
Here are extra tips to guide you in making an informed decision:
Involve your translation teams
The first-hand users of the MT engine, your translators, and localization experts, can offer in-depth and practical insights. They understand the complexities of translating different types of content and languages, and can provide feedback on how well potential engines might cater to their needs.
Gather their feedback on factors such as the engine speed, ease of use, quality of translations, and scope of post-editing. This feedback can serve as a valuable resource in your decision-making process.
Combine assessments with research
Complement your internal assessment with external research.
Examine customer reviews, ratings, case studies, and whitepapers from the engine providers. Research industry-specific forums or online communities for additional, unbiased insights. This dual approach can give you a balanced view of the engines' capabilities and suitability for your requirements.
Sample in advance
Taking a potential translation engine for a trial run can provide tangible insights. Most providers offer free trials or demo versions, allowing you to test the engine with your specific content and language pairs. This hands-on experience can offer a sense of how the engine might perform under your actual operational conditions and help identify any potential issues before making a commitment.
Get a free demo of our translation management system now!
Prioritize data security
Data security should be a top priority, especially if you handle sensitive or confidential content. Ensure the engine has stringent measures to protect your data, including end-to-end encryption, secure data storage, strict access controls, and adherence to international data privacy regulations like the GDPR.
A breach in data security can have legal implications and damage your reputation.
6 Use Cases for Machine Translation
Machine translation engines are now a common choice among SMBs and enterprises alike due to their multiple benefits. While they offer speed, affordability, and high text volume capacity, it's crucial to understand when their use is most effective.
Machine translation (MT) stands out for its speed. MT tools can almost instantly translate large volumes of text making them invaluable for urgent or real-time translation needs, such as international webinars or live meetings.
According to a CSA Research study, 76% of online shoppers prefer to purchase products with descriptions in their native language. Furthermore, 40% of online shoppers will not buy from websites that are not in their native language. For businesses to meet such demands swiftly, machine translation can often be the most efficient solution.
Use case #2. High content volume
Machine translation engines come in quite handy when dealing with large volumes of content. Tasks that would take humans months to complete can now be finished in mere minutes thanks to the MT’s capacity to handle large amounts of data quickly.
According to a Common Sense Advisory analysis, the use of machine translation engines in the language services sector grew from 13% to 24% of all projects in the 2019-2020 period. The rise of machine translation’s popularity can be attributed to its adroit handling of high-volume projects, which is unparalleled by its human counterparts.
Use case #3. Tight budget
Machine translation tools may be a more cost-effective choice for businesses on a tight budget than expert human translators. Although human translators can provide translations of a high caliber, their services are frequently more expensive, especially for bigger projects.
This makes it a desirable choice for companies wishing to manage substantial translation needs while maximizing their translation costs. So, if you aren’t translating anything particularly creative and affordability is your top priority, opt for MT.
Use case #4. Rough translations
Although MT engines have improved significantly over the years, their translations might not always match the accuracy of a skilled human translator, especially when it comes to nuanced or complex texts. For instance, machine translation engines often struggle to accurately translate professional and business content, user instructions, and sales and marketing materials due to their intricate nature.
However, in scenarios where the highest level of accuracy isn't required, such as internal communications or understanding the gist of a document, machine translation engines can be a convenient and swift solution.
Use case #5. Unavailable human translators
Machine translation engines can also fill in the gaps when human translation is unavailable, whether because the language pair is rare or as a result of a strict budget. Machine translation, with its round-the-clock availability, allows organizations to maintain the flow of communication across different languages without waiting for human translators.
Use case #6. Simultaneous projects
When juggling multiple translation projects at once, MT engines can offer the benefit of parallel processing. Specifically, they are capable of handling several tasks concurrently, thus enhancing productivity and saving time.
In essence, machine translation engines are powerful tools for specific translation needs. They offer speed, cost-effectiveness, and adaptability, especially for high-volume tasks or when human translation is unavailable. The ongoing advancements in AI and machine learning promise further refinement of these engines and an expansion in their applicability. Thus, the question is not if but when to deploy TMs for optimal effectiveness.
To Sum Up
Choosing the best MT engine requires a multifaceted assessment process. Every factor is important, from evaluating the content type, language pairs, and translation accuracy to comprehending price structures, legal compliance, data protection, and memory support features.
There's a lot to consider.
Fortunately, you can make a more educated choice by incorporating internal feedback, completing external research, and testing engine options.
Centus' goal is to streamline this complicated procedure. As a comprehensive translation management system, we integrate your chosen MT engine into your workflow, allowing you to focus on selecting the right engine while we handle the translation management side of your business. Learn more.
Which machine translation engine is best?
The "best" MT engine depends on your specific needs and requirements. Each engine has strengths and limitations, and you’d need to assess factors such as content type, language pairs, pricing, legal compliance, system compatibility, support services, data privacy, and more.
At the same time, the most popular MT engines are Google Translate, Google Cloud Translation, Amazon Translate, DeepL, and ChatGPT.
How do you evaluate machine translation models?
Machine translation models can be evaluated through human assessments or automatic assessments.
Human evaluation is the review and rating of the translation quality by professional translators. Automatic evaluation employs metrics such as BLEU, METEOR, or TER to measure translation accuracy objectively. Both approaches provide valuable insights into the performance of machine translation models.
What are the 3 main techniques used for machine translation?
The three main techniques used in machine translation are:
Rule-based machine translation (RBMT): This method relies on linguistic rules and bilingual dictionaries. The source language's grammatical rules are used to convert text into the target language.
Statistical machine translation (SMT): This method uses statistical models and algorithms, learning from large amounts of bilingual text to translate from the source to the target language.
Neural machine translation (NMT): This method utilizes artificial neural networks to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.
What are the 4 types of machine translation in NLP?
In Natural Language Processing (NLP), there are four main types of machine translation:
Rule-based machine translation: Uses grammatical and linguistic rules to translate from one language to another.
Statistical machine translation: Uses statistical models based on bilingual text corpora to generate translations.
Example-based machine translation: Relies on a database of source-target language pairs and uses these examples to guide translation.
Neural machine translation: Leverages deep learning, especially recurrent neural networks (RNNs), to provide more fluent and natural-sounding translations.