What are the features of Machine Translation?
Get deeper knowledge of Machine Translation (MT), the future technologies, and the strategies of implementing MT tools into localization workflow.
The chances are that most of us today have used machine translation in some way, for example, Google Translation. Digitization of almost all business processes erased the distance. However, the language gap was still crucial for spreading the information throughout the digital environment evenly or vice versa, acquiring the data from the internet. The importance of translating tools increased, and today the existing ones are working on better productivity while the new ones are being created on totally new technologies.
Machine Translation, the trending technology, and today’s topic is the automated process of rendering one language to another within one source. To understand the technology, let’s dig deeper into the processes of Machine Translation.
What is Machine Translation?
Automated translation or Machine Translation (MT) is a set of tools enabling users to input text in one language and get it translated into a target language without further human intervention. While human translation is based on the linguistics and grammatical knowledge of a translator, it is much slower and less productive, especially when it comes to large volumes of content. Meanwhile, MT is much quicker based on machine learning, analytics, and probability. The translation may not always be accurate, but the technologies evolve fast, the translation tools become more intelligent and precise, bridging the gap between fluency and accuracy.
What does MT mean for translators?
Just like any new technology development is presumed as an attempt to replace humans with robots, Machine Translation caused controversial emotions among the experts, particularly among the translators. One common misconception is that human translators will soon become redundant.
Machine Translation development pace may predict the future, where the MT is as accurate as human translators. However, human linguists will continue playing a significant role in MT-driven translation workflows and the actual development of the technology, adapting and ensuring its consistent quality.
The History of Machine Translation
It was the early 1950s when the idea of Machine Translation became a reality. Throughout history, technology has undergone changes with four prominent milestones that define the history of MT.
1949, the concept of Machine Translation. Warren Weaver of the Rockefeller Foundation proposed a principle of automatically translating one language to another. A few years later, in 1954, The Georgetown MT research team held a public presentation with a physical device.
1966, the US Automatic Language Processing Advisory Committee (ALPAC) was formed. The committee found the industry ineffective and not worth the trouble or expense.
1997, MT makes its way into the mainstream with internet penetration. Despite the decreased interest in machine translation, globalization pushed the largest technology companies to adopt the technology as a cost and time-saving solution for document processing.
2013, Statistical Translation is live. The technology is widely used by everyone trying to automate translation operations or support human translation by automating part of the workflow.
Present day; Neural Machine Translation is forging. The new technology is predicted to be more accurate as human translation and more productive.
Machine Translation in 2022
2021 was a game-changing year in the development of Machine Translation amid the rapid expansion of AI technologies, such as voice synthesis, speech recognition, and more. The businesses also started implementing MT technologies to facilitate customer support services.
2022 will bring some significant changes into the segment.
- More local languages support
- Better context handling
- More clever quality estimation metrics
- New places disrupting the MT engines
- Better collaboration between MT and humans
- Textless speech translation
Machine Translation System Market 2022: Industry news
In 2020, the value of the global Machine Translation market was $153.8 million. It is expected to grow at a CAGR of 7,1% and be valued at $230.6 million by 2026. Some of the fundamental driving forces of market growth are increasing demand for content localization and the need for high-speed translation. North America remains the most prominent market for MT due to its growing IT sector demand.
What are the benefits of Machine Translation?
Machine translation without human intervention is perfect for low visibility texts and user-generated content. In other cases, the translated content should proceed with proofreading. In any case, the advantages of Machine Translation are apparent, yet it is worth mentioning significant benefits increasing translators’ capabilities up to 5 times.
Time and cost efficiency - The most crucial benefit of machine translation is speed. Regardless of the skills and knowledge of a translator (even for native speakers), documentation translation will take much time and effort. Additionally, a human translator is more expensive than a machine translator software.
Communication simplicity - The language gap is no longer a problem with MT. You can easily translate your messages within seconds, even without leaving the page.
Consistency - Modern MT software solutions provide consistent translations significant in business records management.
Security - Hiring an employee to deal with confidential information entails risks; that is why the utilization of MT software guarantees security.
Why do companies use Machine Translation?
Machine translation meets individual and corporate needs. While you use it for personal purposes, businesses leverage Machine Translation software to automate workflow and produce copy in multiple languages. By implementing MT, companies much easier localize e-commerce sites and generate multi-lingual content to reach a world audience. The productiveness of MT helps companies:
How does Machine Translation Work?
In non-technical language, MT uses software that converts X language to Y language. It may sound simple, but it is based on complex processes. Today there are four types of Machine Translation based on different technologies and providing additional productivity.
Rules-Based Machine Translation
Rules-Based Machine Translation (RBMT) was the first model of MT. The system is based on grammatical, semantic, and syntactical rules predefined by human experts for both languages. The translation goes through three phases: analysis, transfer, and generation.
RBTM development is the most complicated, time-consuming, and expensive model that requires manual editing, but it is better in accuracy and consistency.
Statistical-Based Machine Translation
Statistical-based Machine Translation (SMT) doesn’t have language rules; instead, the technology analyzes large amounts of human-translated data for each language pair and builds a model of relationships between words, phrases, and sentences. Then SMT applies the same translation model to a target language and converts the elements.
Thanks to the extensive use of the internet and cloud computing, the Statistical-based system now has higher fluency but may still be less consistent than other types of MT.
Neural Machine Translation
Neural Machine Translation (NMT) is based on deep neural networks; it evolves and teaches itself how to translate. The technology harnesses AI and machine learning to generate translations.
Contrary to traditional MT, Neural Machine Translation mimics or at least tries to mimic a translator's thought process instead of guessing. The result is a more natural translation with nuances. NMT can already be used for large volume documents’ translation and regular business documents.
Finally, Neural Machine Translation has come to solve the gap and shortcomings of MT, like poor readability and incompatibility.
Hybrid Machine Translation (HMT)
The hybrid model combines RBMT and SMT systems leveraging a translation memory and delivering productivity better than SMT or RBMT separately. Nevertheless, Hybrid Machine Translation will still need human editing.
When to use Machine Translation?
Machine Translation is a lifesaving technology that may be used as part of daily workflow but let’s talk about business processes and the necessity of incorporating MT in business.
Working with large volumes of content - Machine Translation is an efficient tool to translate whole websites and large volume documentation within short deadlines. Machines work uninterrupted delivering instant results. Depending on your goals, human translators can be engaged in final proofreading and reviews to ensure the content is polished and accurate. Machine Translation is not suitable for marketing-related texts because it needs more targeted content.
Not focusing on nuances - Machine Translation is efficient in software documentation and manuals where the language is straightforward, and the translation is maximum accurate. Such documentation does not require a polished look. At the same time, legal or medical information cannot be fully trusted to MT.
Limited translation budget - Human Resources always cost more than technologies; this is why we accept MT as a more efficient and cost-saving option. It becomes critical when the budget is limited. Many small-scale businesses already harness machine translation technologies to facilitate content translation and maintain the integrity of translated texts.
Working with ephemeral content - Regularly updated content like emails, FAQs, and customer reviews is mainly created with Machine Translation. The quality can be average and not as accurate as professional documents. MT is also used for in-house research.
Evaluation of Machine Translation
As noted repeatedly, Machine Translation is about the final quality of the content, and sometimes it may be off the desired result. That is why the technologies continue evolving, and soon translation systems may be as accurate as humans. To understand the efficiency of translation, the output is constantly evaluated. There are two methods of evaluation.
Manual Evaluation - This type supposes manual proofreading of the final text. The main criteria are accuracy of the meaning and fluency. The evaluator (human) checks the result solely to ensure it is free of syntactical and grammatical errors. Then the translated text is compared to the original to ensure it delivers the meaning.
Automatic Evaluation - The automatic evaluation needs to ensure the output is close to human translation as much as possible. Based on pre-existing translations, automatic evaluation is often repeated and doesn’t require human interference.
How to start with Machine Translation?
Before starting with Machine Translation for business purposes, consider the following factors, strategies, and technology to implement.
Budget: Each type of MT covers different objectives. At the same time, each type meets different budgets. For example, you may consider SMT for a limited budget, but translation improvement may cost you much if updates are needed. In this light, both NMT and SMT may be equally considered for implementation.
Industry: Industry-specific translations like technical documentation require more sophisticated processing that can be achieved with the NMT method.
Language Pairs: Machine Translation methods are effective for specific language pairs. For example, SMT works best for Latin-based languages with similar syntax and linguistic rules; they are the most compatible with machine translation.
Amount of Content: Machine Translation works great with large volume content, but NMT is the best to process tons of documentation. The system will get faster and more precise during translation.
Customer-Facing vs. Internal Content: Customer-facing content (marketing texts reflecting brand identity) needs high-quality translation. In these terms, such content is translated either manually or through a combination of machine translation and human post-editing. MT can be a quite effective, time and cost-saving solution for internal communication and documentation.
Machine translation post-editing (MTPE): Ideally, present-day machine-translated content needs to be processed with human post-editing to ensure accuracy until the technologies become too smart to be 100% accurate. Depending on the objectives and requirements, the content may be processed through two post-editing techniques:
How to implement Machine Translation?
Once the machine translation strategy is specified, you can start integrating the system into the workflow, aligning and changing the processes accordingly. To not turn the implementation into a daunting task, there are several machine implementation steps to follow.
- Choose the right content for machine translation.
- Train the system with specific data to increase the output accuracy if the engine requires training.
- Select a team of professionals for post-editing.
- Test the engine to identify the strengths and weaknesses and improve them before implementing the system into the workflow.
- After final deployment, follow the system's development and results to track the progress.
Final Words: The future of Machine Translation
Over the years, machine translation has made tremendous improvements in helping us localize content for a global audience. Machine Translation can produce high-quality output with minimal human involvement with proper implementation. Although MTs are not used alone, they can be extremely useful when there is a great deal of content to translate where the human translation is impossible.
The expansion of Neural Machine Translation and AI technologies will soon bring automated voice translation solutions, speed, and productivity that may dramatically transform the workflow. Stay in touch to learn the latest news and updates in the industry.
Frequently Asked Questions
Machine Translation (MT) is a set of tools enabling a user to input text in one language and get it translated into a target language without further human intervention.
The advantages of Machine Translation are obvious, yet it is worth mentioning significant benefits increasing translators’ capabilities up to 5 times.
Time and cost-efficiency
MT uses software that converts X language to Y language. Today there are four types of Machine Translation based on different technologies and providing additional productivity.
Rules-Based Machine Translation
Statistical-Based Machine Translation
Neural Machine Translation
Hybrid Machine Translation