Google Translate Expansion Opens Up Machine Translation Vs Human Translation Debate Once More

At Google I/O 2022, held on 11 and 12 May, the firm revealed that its popular Google Translate software now encompasses 24 additional languages, taking the total to 133. The most recently added languages include tongues from Africa, Asia and the Americas.

 

The news of the additional languages, which I’ll cover in more detail below, means that businesses using machine translation software now have more indigenous and rare languages at their fingertips than ever before. With that in mind, let’s fire up the whole human translation vs machine translation debate once more.

 

The Case for Human Translation

 

Can a human translate better than a machine? This is the question that many businesses ask when they’re weighing up the human versus machine translation decision. But the answer depends entirely on what we mean by ‘better’.

 

A human translator can deliver greater nuance and refinement, certainly. This is central to the case for human translation. Machine translation software doesn’t yet provide all those little tweaks and adjustments to the text that make human translators’ work such a skilled undertaking. Ambiguous expressions, idioms, gendered language… all of these benefit from a watchful human eye (you can read more on how to translate gender by clicking the link below).

 

However, the machine translation vs human translation debate doesn’t end there. Because machines can translate much faster than humans. And what cost-conscious business can afford to ignore that fact? 

 

Read more: How to Translate Gendered Language

 

Human Vs Machine Translation

 

The potential cost savings are why there has been such a sharp increase in demand for machine translation post-editing (MTPE) services in recent years. Businesses are using machines to translate fast, then humans to refine the translations. It’s no longer a question of simply human translation vs machine translation, but rather of taking the best that each can provide and blending them. The speed of machine translation is complemented by the unique touch that human translators bring to the table.

 

If you’re after an in-depth exploration of machine translation post-editing, the link below should help.

 

Read more: The Ultimate Guide to Machine Translation Post-Editing

 

Which Additional Languages Does Google’s Machine Translation Software Include?

 

The latest additions to Google Translate hail from Latin America, Asia and Africa. Collectively, they have around 300 million speakers. Businesses looking to connect rapidly and easily with speakers of these languages now no longer have to rely on human translation alone.

 

The full list of 24 addition languages includes:

 

Latin America:

·  Quechua (around 10 million speakers)

·  Guarani (approx. 7 million speakers)

·  Aymara (approx. 2 million speakers)

 

Asia:

·  Bhojpuri (approximately 50 million speakers)

·  Maithili (approx. 34 million speakers)

·  Assamese (approx. 25 million speakers)

·  Ilocano (approx. 10 million speakers)

·  Kurdish (Sorani – approx. 8 million speakers)

·  Dogri (approx. 3 million speakers)

·  Meiteilon (Manipuri – approx. 2 million speakers)

·  Konkani (approx. 2 million speakers)

·  Mizo (approx. 830,000 speakers)

·  Dhivehi (approx. 300,000 speakers)

·  Sanskrit (approx. 20,000 speakers)

 

Africa:

·  Lingala (approx. 45 million speakers)

·  Oromo (approx. 37 million speakers)

·  Luganda (approx. 20 million speakers)

·  Sepedi (approx. 14 million speakers)

·  Bambara (approx. 14 million speakers)

·  Twi (approx. 11 million speakers)

·  Tigrinya (approx. 8 million speakers)

·  Tsonga (approx. 7 million speakers)

·  Ewe (approx. 7 million speakers)

·  Krio (an English dialect – approx. 4 million speakers)

 

What Impact Will This Have on Human Translators?

 

Humans who translate these languages aren’t going to be put out of business overnight by Google’s machine translation software advancements. However, it would be wise for them not to ignore machine translation either. Those who can rapidly update their business model to offer MTPE services for these languages will be well positioned to take advantage of a new wave of demand.

 

Businesses will have some decisions to make as well. A fundamental question of decisions around machine translation vs human translation is, ‘How good is good enough?’. For some businesses, only the highest quality of translation will suffice, in which case the finesse of a human translator will be essential. In other cases, something that is translated well enough to convey the intent of the original, without needing to be word-perfect, may be good enough.

 

The Future of Human Vs Machine Translation

 

One of the most exciting elements of Google’s reveal is the tech that lies behind the addition of the 24 languages. The firm has used ‘Zero-Shot Machine Translation’ technology, meaning that it only needed to feed monolingual text into its machine learning model. Other models need parallel (translated) text from which to learn, but the Zero-Shot MT model can deliver translations “without such data,” according to Google.

 

While the technology isn’t flawless yet, it carries major implications for adding new languages to Google Translate over the coming months and years. And this has major implications for companies looking to overcome language barriers in order to maximise their potential (for more on that, click the link below).

 

Ultimately, businesses that can save time and money without seeing standards slip have the potential to outperform their competitors. Advances in machine translation are supporting businesses to do this, particularly when human translation expertise is added into the mix.

 

All of this points very clearly to machine translation, and machine translation post-editing, being the way forward for the translation sector. The outdated human vs machine translation debate has been replaced by an ever-closer mixing of the two approaches, with human translators’ skills complementing machine translation software’s speed.

 

This new paradigm is enabling businesses to reduce the time their translations take, as well as the cost of producing them. It is also supporting them to reach out and connect with new audiences, thus opening up new revenue stream potential and new possibilities on a global scale. 

Read more: Translating in digital age

FAQs

How does machine translation handle dialects and regional language variations?

Machine translation often struggles with dialects and regional language variations due to their specificity and less common usage in training datasets. While mainstream languages are generally well-represented, dialects may not be as accurately translated. You can improve results by customizing the translation engine with localized data or using post-editing to refine machine translations. Human translators excel in this area, as they understand cultural and contextual nuances, ensuring accurate and culturally relevant translations.

What are some common errors in machine translation that human translators typically avoid?

Machine translation often makes errors like literal translations, which miss context and idiomatic expressions. It can also struggle with homonyms and complex sentence structures, leading to misinterpretations. Cultural nuances and tone are other areas where machines falter. Human translators, with their deep understanding of language and context, avoid these pitfalls by ensuring that translations are accurate, contextually appropriate, and culturally sensitive.

How is the quality of machine translation evaluated and improved over time?

The quality of machine translation is evaluated through metrics like BLEU scores, which compare machine outputs to human translations. Continuous feedback from users helps identify errors, and retraining the model with updated data improves accuracy. Incorporating post-editing by human translators also refines the output. Regular updates and advancements in AI algorithms further enhance the performance and reliability of machine translation systems.

How do advancements in AI and machine learning affect the future of machine translation?

Advancements in AI and machine learning are significantly enhancing machine translation capabilities. Improved algorithms and larger, more diverse datasets are leading to more accurate and context-aware translations. Neural machine translation (NMT) models are particularly promising, offering better handling of context and nuance. As AI continues to evolve, you can expect machine translation to become increasingly reliable, reducing the gap between machine and human translation quality, and making it a more viable option for various applications.