3. Why use NLG?
Natural Language Generation (NLG) is the process of using artificial intelligence to generate language for various purposes. NLG systems are used in a variety of contexts, such as chatbots designed for customer service or automated question answering services like Siri and Alexa.
While these systems are not yet capable of mimicking human speech without conceptional guidelines provided by a user or programmer, they can produce text and sentences which sound natural.
Most systems have underlying deep learning models to learn NLG techniques, such as:
which are probably the most commonly used techniques.
In most cases, a single NLG technique is not sufficient to generate language. The following image shows quite well how we can combine text structuring, lexicalization and linguistic expression generation to create a whole sentence:
This example shows how text linguistic realization allows software to create text outputs from a JSON document. In this case, text structuring places the adjective behind the noun instead of leading to a sentence such as "This is not a cool woman.". Finally, lexicalization is responsible for the correct flection of the determiner (as it would be "These" for plural cases) and the verb.
Now imagine if we switched the underlying data. We could switch up tenses, the amount of nouns and adjectives and much more, while the generated text would still be grammatically correct due to our NLG techniques.
Possible outputs might be:
The advantage: grammatically correct text generations.
The prerequisite: available structured data.
NLG can be used in many different ways. Based on our experience the most business value can be generated by using NLG as:
There are many reasons to use NLG instead of copy writing resources. You see, the age of manual content writing is over - at least when it comes to creating thousands of similar, yet unique texts. By similar we mean that these texts follow a similar logic.
Manual content writing en masse is a very tedious process and not pleasurable at all, neither for employees nor for contracted content writers. Our goal with NLG is to leverage the creativity and professionalism of content writers to design great text concepts. These concepts can then be implemented as text models to generate away incredible amounts of content.
Instead of creating single texts manually we can now create a concept for certain text categories, such a product families, same types of reports or texts with similar sentence and paragraph structures.
This is a colossal gain when it comes to scalable and extraordinarily fast generation of relevant content. Who cares if you can use run-of-the-mill AI content writing software such as Jarvis AI to generate cheap non-personalized text results if the results are not personalized, missing relevant information or even wrong?
With our software solution we enable true scalability because you can:
And the best part? You don't have to audit each and every single result since you control your text logic and structure, not a fully automated AI. You need a high performing assistant to generate your ideas within seconds, not a software that thinks that it does your job better than you.
Here the most common application areas are Automated Product Descriptions, Category Descriptions and Meta Titles. Every content of a product page that is customized by a content management system (CMS) can be filled with automated texts.
This has many advantages, starting with obvious ones like faster texting of new products, optimizing by keywords and avoiding duplicate text.
But also addressing the customer through a targeted user journey and personalized content lead to an average of 10% higher conversions.
Report automation is the process of using software to generate reports, based on data inputs. It's most often used in finance, banking and pharma fields where large volumes of reporting are required. Each report can be uniquely written and the quality is the same or better than hand written ones.
The benefits of report automation include cost savings, increased accuracy and efficiency, and reduced time spent generating reports.
Our NLG software generates reports that really want to be read, for example on company valuations or clinical studies.
In the last few years, with advances in artificial intelligence and machine learning, we've seen an increase in the number of automated sports coverage.
This can be expanded to weather reports, pollen weather, crime reports, horoscopes, etc.
Currently, highly automatic AI-based generation is not suitable for commercial scenarios where input is usually high-volume and heterogenous. Therefore, rule-based systems or hybrid systems that combine rules and NLG techniques are currently prevailing in quality and scalability because:
Future developments might look at more situated language generation where environment, style and personality take impact on the generated text. This would enable chatbots to adapt to certain slangs or a tone of voice that is suitable for their human vis-a-vis. Researches are also pouring a lot of focus into social media research where non-canonical language (slang, irony, etc.) are widely available. Non-canonical language poses an even bigger challenge than generating natural language.
Need help with your eCommerce copywriting? You’ll want to read these tips before pulling the trigger.
Most product descriptions are doing it wrong. Here’s why you should care and what you can do about it - in 10 sure-fire steps.