Top Tips For Testing Natural Language Processing Performance, By Shama Ugale;

Top Tips For Testing Natural Language Processing Performance, By Shama Ugale;

Natural Language Processing (NLP) is a space that has evolved rapidly in recent years with endless possibilities for application in the real world. As a result, we have come a long way from command line interfaces to conversational interfaces, with Artificial Intelligence (AI) and Virtual Reality (VR) emerging as exciting technologies to bring NLP to the next level.

The rapid adoption of conversational interfaces among many sectors has resulted in significant progress in terms of the capabilities of chatbots – as we quickly moved from rule-based bots to AI-based bots, powered by machine and deep learning techniques, which use NLP to understand and process the human language.

As with any technology, it is vitally important that such software is evaluated in terms of performance before a decision is made regarding whether a model is ready to go to production. With that purpose in mind, here are a few tops tips for testing NLP performance…

Test early & regularly;

It might sound obvious but the more testing you do, the better your model will be. Like any app, it is good to have a couple of build stages and then test to validate whether the model is acceptable or requires more work. The build should only progress to the next stage when you are satisfied with the results of the testing. Furthermore, this process should be repeated after any amendments to the dataset or additions of new features to ensure that the algorithm is learning and predicting accurately in line with the changes, as well as the needs of the users.

Define KPIs

Formulating metrics for evaluation and analysing these results on an ongoing basis will enable you to create a kind of matrix which will produce a summary of all test results, including correct and incorrect predictions and classifications. You can then use these insights into the performance of the model to further train it and correct the outcomes. Such metrics might include accuracy, recall, confidence and precision.

Focus on training

Sometimes errors can arise from limitations in training which, again, is why repetition of testing is key. It can be useful to treat an NLP model like a toddler – for example, when learning how to identify objects, it is important to introduce variety so that children can identify objects which are the same but have different characteristics (so not just shape but colour, patterns, etc.). Algorithms need to be able to do this as well, which means you need to test thoroughly, with different situations or samples that require the same solution, otherwise issues can arise during the identification process.



Ensure data is right

Peter Gentsch (an expert in AI marketing, sales and service) once said: “To the user, chatbots seem to be “intelligent” due to their informative skills. However, chatbots are only as intelligent as the underlying database”. In other words, bots are only intelligent because of the data upon which they are based, so make sure you have the right dataset and the right insights into this data so you can train and develop the model effectively.

You can also use open source tools, such as Botium Coach, to test the performance of NLP models. However, the core approach should remain the same – test, test and test some more! The more tests and insights you can garner, the better your model will be in terms of accuracy and precision when it goes to production. As more personalised and sophisticated bots emerge, this element of the software development process will become even more crucial and valuable.


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