Machine translation (MT) is the use of computer software or artificial intelligence to automatically translate text or speech from one language (known as the source) into another (the target language).
Machine translation provides translations based on computer algorithms, with no human participation, and should not be confused with Computer Aided Translation (CAT) which is often used by professional language services providers (LSP) in a supportive manner to the human translator in order to improve speed, accuracy and effectiveness for repeat or common translations with popular language pairs.
The most well-known machine translation engines are:
Some of the above offer free translation packages which are really useful in certain situations such as, while on holiday in a foreign country, or when learning a new language.
However, if using them in a professional setting such as, translating a website for an overseas audience, or translating important documents, they should be used with caution and checked for error by an experienced human translator whenever possible.
How does machine translation work?
While there are various approaches to machine translation, the most prominent and effective method in recent years has been neural machine translation (NMT).
Here’s a simplified overview of how machine translation, specifically NMT, works:
Data collection
In order to train a machine translation model, a substantial amount of bilingual data is used. This typically consists of parallel corpora, which are collections of sentences or texts in one language aligned with their corresponding translations in another language. This data is crucial for teaching the model how words and phrases in one language correspond to those in another.
However, it is also one factor in explaining why it isn’t always accurate as it operates using a predictive method which assumes sentence structure, grammar, syntax and spelling.
Neural Network Architecture
NMT relies on deep learning neural networks, specifically Recurrent Neural Networks (RNNs) or Transformer models. Transformers have become the dominant architecture for NMT due to their superior performance. These models are composed of multiple layers of artificial neurons that process and generate translations.
They are designed to mimic the human brain through their ability to process and generate information.
Training
During the training phase, the neural network is fed with pairs of sentences in the source language and their translations in the target language. The network learns to predict the target language sentence given the source sentence. This is achieved through the optimisation of model parameters to minimise the difference between the predicted translation and the actual translation in the training data.
Training is an iterative process that adjusts the model’s weights until the translations generated by the model closely match the human translations in the training data.
Encoding and decoding
The neural network consists of an encoder and a decoder. The encoder processes the input sentence in the source language and transforms it into a series of numerical representations (vectors). The decoder then generates the translation in the target language by using these representations.
The encoder and decoder work in tandem to capture the meaning and structure of the source sentence and generate a coherent translation.
Attention mechanism
A key innovation in NMT, particularly in Transformer models, is the use of an attention mechanism. This mechanism allows the model to focus on different parts of the source sentence when generating the translation, mimicking the way humans pay attention to relevant words and phrases during the translation process.
Inference
Once the NMT model is trained, it can be used for translation. During the inference phase, a source sentence in the input language is processed by the encoder, and the decoder generates the corresponding translation in the target language.
This is typically done word by word, with the model predicting the next word based on the previously generated words.
Because of this, its accuracy can should not be relied upon without human supervision and proofreading.
Post-processing
The generated translation may undergo post-processing steps, such as reordering words, handling idiomatic expressions, and ensuring grammatical correctness.
Evaluation
Machine translation systems are evaluated using various metrics, including BLEU (Bilingual Evaluation Understudy) and human judgment. These metrics help assess the quality and fluency of translations and guide improvements in the model.
Machine translation continues to advance with the development of more powerful neural network architectures, larger training datasets, and improved training techniques, making it increasingly accurate and useful for a wide range of applications.
However, it has also been shown in recent tests to be a system which can offer biased translations, especially relating to gender, disability and race.
This is far from ideal if the translation is to be used in a professional setting or a setting in which the above factors are particularly relevant, such as, medical or legal translations.
In these instances, it is preferable to use human translators, who are native speakers of the language and have an innate understanding of cultural and linguistic nuances which might affect the intended audience.
If you would prefer to use native-speaking human translators for your translation project and require a professional language service which is second to none, contact the team at Creative Word now.
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