Jul 14

Multilingual Natural Language Processing: solutions to challenges

Natural language processing (NLP) is no longer foreign for many computational industries, as it is helping humans communicate with computers and vice versa.  

Most existing NLP innovations in the world so far have a strict focus on English as a language. As there are over 7,100 languages in the world, it’s extremely challenging to develop NLP for all of them.  

Beyond doubt, there has been an increasing need for NLP solutions for other languages. Yet, the difference between the amount of English and non-English NLP models stays clearly imbalanced. It’s important to build and develop multilingual NLP - NLP in more languages other than English - to serve markets around the world more effectively. 

What is multilingual Natural Language Processing (NLP)? 

Multilingual NLP is a technology that integrates linguistics, artificial intelligence, and computer science to serve the purpose of processing and analyzing substantial amounts of natural human language in numerous settings. 

AI robot hand pressing a keyboard button to run multilingual NLP

How does multilingual NLP work? 

There are many different forms of multilingual NLP, but in general, it enables computational software to understand the language of certain texts, along with contextual nuances. Multilingual NLP is also capable of obtaining specific data and delivering key insights. In short, multilingual NLP technology makes the impossible possible which is to process and analyze large amounts of data. Without it, this kind of task can probably only be executed by employing a very labor- and time-intensive approach. 

What makes multilingual NLP difficult to scale? 

One of the biggest obstacles preventing multilingual NLP from scaling quickly is relating to low availability of labelled data in low-resource languages. 

Among the 7,100 languages that are spoken worldwide, each of them has its own linguistic rules and some languages simply work in different ways. For instance, there are undeniable similarities between Italian, French and Spanish, whilst on the other hand, these three languages are totally different from a specific Asian language group, that is Chinese, Japanese, and Korean which share some similar symbols and ideographs.  

The outcome from this leads to the need to have various techniques to generate language models that can work with all these languages. In short, different languages often require different vector spaces, even if there are existing pre-trained language embeddings. 

Even though pre-trained word embeddings in different languages exist, it is possible that all of them are in different vector spaces. This means that similar words can signify different vector representations, basically due to the natural characteristics of a certain language. 

This is why scaling multilingual NLP applications can be challenging. They use large amounts of labelled data, process it, learn patterns, and generate prediction models. When building NLP on a text comprising different languages, it is best to consider multilingual NLP. 

When we need to build NLP on a text containing different languages, we may look at multilingual word embeddings for NLP models that have the potential to scale effectively.  

woman talking to her phone for multilingual voice command

Solutions for tackling multilingual NLP challenges 

1, Training specific non-English NLP models 

The first suggested solution is to train an NLP model for a specific language. A well-known example would be a few new versions of Bidirectional Encoder Representations from Transformers (BERT) that have been trained in numerous languages.  

However, the biggest problem with this approach is its low success rate of scaling. It takes lots of time and money to train a new model, let alone many models. NLP systems require various large models, hence the processes can be very expensive and time-consuming.  

This technique also does not scale effectively in terms of inference. Using NLP in different languages means the business would have to sustain different models and provision several servers and GPUs. Again, this can be extremely costly for the business. 

2, Leveraging multilingual models 

The past years have seen that new emerging multilingual NLP models can be incredibly accurate, at times even more accurate than specific, dedicated non-English language models.  

Whilst there are several high-quality pre-trained models for text classification, so far there has not been a multilingual model for text generation with impressive performance.   

3, Utilizing translation 

The last solution some businesses benefit from is to use translation. Companies can translate their non-English content to English, provide the NLP model with that English content, then translate the result back to the needed language.  

As manual as it may sound, this solution has several advantages, including cost-effective workflow maintenance and easily supported worldwide languages.  

Translation may not be suitable if your business is after quick results, as the overall workflow’s response time must increase to include translating process. 

speech bubbles of different languages

NLP solutions from StageZero 

NLP multilingual approaches have gradually gained attention within the field due to the increasing consciousness of the constraints possessed by monolingual and English-only approaches, together with the awareness that one language alone such as English cannot represent the diverse global linguistic reality. 

Multilingual NLP is definitely not a solved issue, but there has been tremendous progress in the past few years and we predict that this topic will become increasingly relevant as companies and their customers gain deeper understanding on the importance of it. 

StageZero has been focusing on hard-to-reach languages for years. From sourcing difficult-to-reach languages, to processing the data to give 10% higher accuracy, our customers trust us to enhance their ML projects through more precise training data

Our experience gives us valuable insights into the everyday reality of such projects, and we’re proud to have helped customers to gain significant value in their projects, such as 50% reduction in time to value for their multilingual applications.  

Is your company looking to scale multilingual NLP more efficiently? Ask us to dive into more detail on how we can save you approximately 50% time to value on your projects! Speak to our partnership team here. If you’re curious or read more on this topic, check out our blog. 

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