Real-Time Language Translation Solution for Chinese People
By: Ayesha Arshad Posted on Tue, 07-05-2024
Translation is a more popular and broader phenomenon than it used to be. It is a necessity these days in almost every field of life, education, business, recreation, hence everywhere. And with the concept of getting solutions as fast as one can get, the notion of real-time translation solutions becomes very important.
Real-time translation is not just the use of apps like Google Translate and DeepL, rather it revolves around the idea of getting translation solutions right then and there at the moment which is why it is called real-time translation. For instance, if you are searching for something on the Internet and the page is in Russian, here Google Translate will translate the whole page into your native language with minimal errors. But in situations when you are talking to a person who speaks a different language, Google Translate won’t be of much help.
In this read, we are going to talk about real-time language translation tools such as machine translation, NMT, NLP, speech recognition and so much more. Other than that, we will also talk about factors that are necessary to look out for when going for real-time translations for the Chinese speakers.
Table of Content
What is Real-Time Language Translation?
It is real-time language translation that makes sure that communication goes freely and smoothly despite the language differences. This translation solution does not depend on human translation which takes time to translate the source. Here are technological solutions that take no time and people get to understand foreign languages quickly. Real-time translation makes instant communication easier and faster.
For instance, you are chatting with someone who speaks a different language than yours, but you both can understand each other easily. This is the magic of real-time translation.
The main element that makes real-time translation more approachable is that they are accessible for every platform. It means that it's web-based platforms or smartphone apps, and real-time translation tools easily work on all of them.
The Epic Need of Translation for the Chinese Speaking Community
Chinese is today the most spoken language in the whole world after English with more than 1.1 billion speakers. More than 929 million people use Chinese as their native language. These Chinese speakers, when need to communicate with the worldly audiences who speak different languages, need translation solutions. Of course, language translation services can be helpful but what if the translation solutions are required in real time?
Imagine you are a Chinese speaker and are visiting another country, let’s say, Spain for tourism. You won't be able to speak Spanish so you will need a translator with you all the time. But what if in some scenarios, the translator is not with you? This is where real-time translation becomes a must for you.
Real-time language translation (RTLT) systems will convert the spoken words as well as written words into digital text and that's how Chinese speakers can easily understand the foreign language. These real-time language translation systems also have speech recognition features that bring the speech into translated written text in no time.
China is becoming the global economic powerhouse. This makes collaboration with other economic powers quite necessary for China. Language barriers can hinder this international partnership so real-time translation becomes a must for the Chinese people.
Real-Time Language Translation Technology
Now let's talk about the real-time language translation technology which is erasing communication gaps. This makes it easy for Chinese speakers to build connections with people who speak different languages. This process fosters great multicultural relationships and makes the world a better place to live for humans where they can interact as well as be understood.
Machine Translation (MT)
First of all, let's talk about what machine translation is. Machine translation is the use of machine learning approaches to translate text from the source language to the target language. Machine learning approaches include artificial intelligence. It uses computational linguistics to analyze, understand, and later translate multilingual data for the Chinese audience. In 2022, the value of MT was estimated to be $982.2 million USD and by 2032 the number will increase to $7.5 billion USD.
| Based on | Why MT? |
|---|---|
| Computational algorithms and linguistic models that fasten automated translation | Faster delivery than human translations |
| Statistical machine learning (SMT) which analyzes parallel texts and learns statistical patterns | Easily accessible on different platforms, smartphones, web browsers, laptops, etc. |
| Focuses on rule-based translations which depend on grammatical structures and linguistic rules to give real-time translation solutions | Can handle large volumes of translations while maximizing accuracy and precision |
Neural Machine Translation (NMT)
NMT is a more revolutionized process and one step ahead of MT. It involves a deep learning approach that consists of neural networks. NMT takes over the old statistical MT and uses neural networks that provide real-time translation solutions while sounding more like humans. Even in speech recognition, these deep neural networks help to achieve accurate translations despite the accent and dialect differences.
It means that all 8 dialects of the Chinese language are identified by AI which gives real-time translation solutions that contain nothing else except accuracy and instant solutions.
It is because of neural networks that real-time translations contain contextual meanings which very much resemble those of human translations.
| Based on | Why MT? |
|---|---|
| Sequence-to-sequence model (Seq2Seq) | Excellent in capturing contextual information which later becomes part of translation solutions |
| Deep learning approach which uses recurrent neural networks (RNNs) | More fluent, faster, and natural than the traditional SMT approach |
| User-feedback mechanism which means that they can be adapted on the basis of the approach given to them and they later generate translation solutions that improve continuously | Adept in understanding double entendre or ambiguity of the source language and later transferring it into real-time translation solutions |
| - | Highly scalable which means that it can easily translate a large amount of data in the current time with no delay |
Natural Language Processing (NLP)
NLP focuses on producing real-time translations which are the result of understanding and analyzing the source language to produce translations that resemble the natural human translations. After breaking the source text into smaller parts, NLP assigns tags to each token (a technical term for the smaller parts). This part of speech (POS) tagging goes for the syntactical analysis of each token, which means that each divided token is given a grammatical tag. This process happens very fast.
After POS comes named entity recognition (NER). This approach saves the data for future use just like what translation memory does for translation management systems. This entity saving module works best for finance, news, and legal documents. The whole focus of NLP is on the semantic relationship between words and phrases and how can this remove ambiguity from translations so that Chinese speakers get to understand foreign languages right then and there at the moment.
| Based on | Why MT? |
|---|---|
| Tokenization approach which breaks the source text into small multiple units such as words and phrases | NLP models break the text on the granular level for providing better translations. |
| POS tagging where grammatical tags are assigned to each token (such as verbs, nouns, adjectives) | Extreme focus and attention to the grammatical structure of the source text to make real-time translation solutions flawless |
| NER which classifies the names of people and organizations for future use | The NER approach preserves names and numerical expressions to maintain coherence in real-time translation solutions |
| With word embedding, NLP captures the semantic similarities between words and generalizes translation solutions | NLP models can be easily customized as per the user preferences which gives fast translation solutions in real time |
How to Choose Real-Time Language Translation Solution for Chinese People
Choosing a real-time translation solution isn't that easy. It depends on several factors such as the setting, tone, speed, and user preference.
Looking Out For Dialects
It is important to ensure that translation solutions support Chinese dialects and variants such as Mandarin and Cantonese which demand higher accuracy. It is vital to verify the real-time translation tools that translate both written and spoken Chinese in real-time. Simply speaking, the compatibility of tools is essential despite the type of input (spoken or written) given to them for translation.
Accuracy, Precision, and Speed of the Tools
Real-time translation solutions need to be accurate and precise so that Chinese people can use them in any setting, be it formal or informal. When it comes to accuracy and faster speed, NLP is always the best and go-to choice.
Machine Translation Vs Human Translations
Despite the prevalent fear about AI and whether it's taking the place of human translators or not, one should know that there is a clear distinction between the both. They both have their own powers and limitations but the main thing upon which they depend is the intended audience and the nature of the text. This means that Chinese translation services are still important as human translations are still in demand.
Last Words!
To sum up the whole read, we can say that real-time translation solutions are actually very important because of their nature of providing translation in person. Real-time translation tools like NMT, NLP, and MT, each have their characteristics which makes them best for different settings and environments.
Machine Translation uses a statistical machine learning approach, and Neural Machine Translation works on the sequence-to-sequence models and recurrent neural networks to provide fast solutions with minimal errors. On the other hand, Natural Language Processing goes for a tokenization approach where it assigns grammatical tags to verbs and phrases also called tokens.
So depending on your goals as a Chinese speaker, you can choose the best translation tool for you and boost communication with the world.
In case you need a professional translation for official documents or your business website, relying on native human translators is a better choice. Here at Mars Translation, we specialize in Chinese language translation. Our team of skilled, native Chinese translators offers fast, affordable, and high-quality translations for all content formats. Get in touch for more details!
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