Introduction

AI translation is a powerful tool that empowers communication between people. With the enhancement of technology, we now have automatic translation provided by machines, which is called machine translation (MT). The usage of machine translation systems spans a great variety of areas: from personal use when traveling to big companies trying to internationalize their products. 

For media companies, machine translation can be extremely useful. If a company wants to deliver subtitles in different languages, a machine translation system can easily speed up this process. Also, if it is a big company, MT can be used for internal communication between team members.

However, the implementation of machine translation in a company is not an easy task. 

Next, I will explain the three main challenging aspects of this process.

Machine translation obstacles

There are three main obstacles when trying to implement MT in a company: scalability, MT evolution, and language support.

Scalability

Big companies generate a lot of data. For example, more than 500 hours of content are being uploaded to YouTube every minute. The amount of data can be overwhelming. For training an in-house model with this huge amount of data, you would need at least these series of steps: 

1. Preprocessing the data to be used as input

2. Training a model that learns from all this data

3. Evaluating the model to check if it works correctly

Moreover, neural network-based systems, which are most used nowadays, need GPUs to be trained and that implies a high cost and robust infrastructure is needed.

Evolution of machine translation

The machine learning field is always evolving. Researchers and engineers are developing better models at a fast pace and it is necessary to keep up to date.

From: https://www.rws.com/blog/evolution-of-machine-translation/

The figure above shows the progression of machine translation models. Evolving from rule-based to statistical MT and then, what is most used nowadays, neural MT. For example, the Transformer architecture is one of the most dominant ones. Transformer-based models achieve high evaluation scores across a range of popular datasets.

This trend shows that, for getting better performance, it is necessary to be aware of the new models that are being created and benchmark their performance to see if it makes sense in your use case.

Language support

Another problem is the language support. There are more than 7,000 languages in the world. Languages, such as English or French, have a bunch of resources available because researchers created a great amount of parallel data over the years. For example, Cho et al. used a parallel corpus of English to French with 38M sentences.

However, to internationalize a product it will have to support many languages. The scarcity of data in some languages is a problem for machine translation systems.

aiXplain comes to the rescue!

At aiXplain, we provide ready-to-use machine translation systems for implementation into your operations. Our team of AI scientists take care of the needed infrastructure by providing API endpoints so that you only have to worry about calling these endpoints and getting the response.

We keep up-to-date models and benchmark them, providing relevant evaluation metrics. That way, you can choose what is most suitable for your use case.

We support a great number of languages and we are always expanding our language collection.

Get in touch with our team here by joining our private beta: aixplain.com.

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