NLU Chatbot Evaluation- 3 Common Errors and 5 Key Steps

The dream of any chatbot developer or product owner is to have an accurate report of how their chatbot is performing. But how can you ensure the continuous improvement of your chatbot?

The "Achilles’ heel" of NLU chatbots is the accuracy to correctly identify the user’s intent and avoid:

1. responding with the wrong intent

2. transferring to a human agent due to lack of confidence, when it should have understood the intent from the beginning

3. responding when it doesn’t have to, instead of passing it on to an agent


The first thing we need to do to prevent this from happening is to know when, how and why these errors happen. Once we have this information, we will be able to retrain and correct NLU training or engine errors.

However, there is no tool on the market that can give us this information accurately - a machine can’t really tell when it’s not doing its job properly, right?

At Bitext, we have created a semi-automated methodology that allows us to tag, analyze and process the data in order to get that precise snapshot of our chatbot’s NLU performance, from which we can improve and evolve it. If you would like to learn more about it, make sure to contact us. 

The idea is simple: a manually curated objective assessment, a detailed report that takes multiple dimensions into account, and a root cause analysis and retraining based on linguistics. No more “black boxes,” and no more throwing our hands up in the air because “this is all we have.”


From now on, if your chatbot doesn’t work properly and you can't even identify the reason why with some certainty, Bitext can help you. If you haven’t already launched you bot, this is the best time to get it right! We recommend that you follow these steps to guarantee the success of the NLU model.

The five key steps would be:

1. correctly identifying your use cases

2. building a comprehensive ontology

3. develop clear definitions for categories, intents and dialogues to avoid overlap

4. generate extensive data, adapted to the user profile for your chatbot

5. put monitoring, evaluation and retraining model in place to produce measurable improvements over time

This is the method that we have developed and have been successfully implementing over the last 2 years. Are you ready to give it a try? Request a demo or let us know if you have any questions or comments. 

Subscribe Here!