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Computational linguistics
Computational linguistics
Computational linguistics

Computational linguistics uses artificial intelligence to help make voice-based interactions between humans and computers feel more natural.

It’s an evolving field of study and development predicated on vocal user interfaces. It aims to accelerate translation, eliminate frustrating customer experiences and enable people to speak directly to computers and receive responses in a more natural, human way.

What is it?

A multi-disciplinary field attempting to understand and digitally model human language.

What’s in it for you?

The chance to deliver stronger, more satisfying, and more natural automated customer experiences.

What are the trade-offs?

New models are powerful but can come with prohibitive training and operational costs.

How is it being used?

Computational linguistics brings important advances to voice interfaces, speech synthesis and assistance tools, along with machine translation programs.

What is it?


Computational linguistics is a multidisciplinary field that seeks to digitally model natural language — helping computers find new ways of understanding the spoken word, and generating their own ‘spoken’ responses.


It has important implications for both customer and employee experiences. Computational linguistics wants to iron out the recognition and interpretation limitations of basic voice recognition technology, and enable all of us to communicate with computers in a friction-free way that feels as natural as talking to another human.

What’s in for you?


The biggest advantage of computational linguistics is that it’s helping to improve the quality of automated customer and employee experiences. Tools like chatbots for example are great at driving down customer service costs, but until now, have been limited in their ability to respond naturally.


Computational linguistics helps solve that problem, through pushing the envelope on automated experiences, moving away from the notion that these should just be delivered at a low cost. Instead, they’re intended to be engaging and don’t drive customers away in frustration.


But its capabilities go beyond just removing the frustration from automated customer interactions. It gives machines a deeper understanding of customer intent and sentiment — helping you direct them to the right place faster, and provide them with better-targeted recommendations that drive revenue while increasing customer satisfaction.

What are the trade offs?


Understanding and modelling human language is complex. It takes a lot of data, and a lot of processing power, which makes it prohibitively expensive and complex for most businesses. Cloud costs for training a single model using the most popular open data sets can easily extend into the millions. As a result, there are currently few viable use cases.


In the past, voice recognition technologies have been found to show significant bias in their ability to understand people and dialects across diverse cultures. While computational linguistics itself seeks to solve that problem and remove the bias, trained models are only as good as the data they’re given — making it easy for bias to creep into even the most advanced use cases.

How is it being used?


Due to the size, complexity and cost of computational linguistics, today it’s mostly being used by research groups. 


Google’s Project Euphonia is breaking new ground in training speech recognition models that can better support users with atypical speech. Mozilla’s DeepSpeech is promising more sophisticated automatic speech recognition engines. And Google’s other research foray, Meena, promises to train on data from public domain social media conversations to deliver a chatbot that can talk to users on any topic.


As research projects like this bear fruit, businesses will eventually be able to make the most of computational linguistics breakthroughs to improve their own voice-based services and offerings.

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