You’ve built your chatbot, you’ve carefully and tirelessly trained and tested it, and you’re finally ready to launch it to go live. But it missed the wrong answer. Then this is the article for you how to fix it.
You’ve built your chatbot, you’ve carefully and tirelessly trained and tested it, and you’re finally ready to launch it to go live — hoorah! But after monitoring its performance over a period of time after go-live, you notice that some user questions return incorrect intents (so give the wrong answers), despite the fact there’s training data in your model that should result in the correct intent being returned. You also spot that some user questions return the correct intent with very low confidence — so the answer isn’t presented to the user. This leaves everyone very frustrated.
As a chatbot builder, you want to understand what influence each utterance has (and even what influence each word in each utterance has) on your model’s performance. Most of the popular NLP providers use a variety of different models and algorithms that are virtually impossible for chatbot builders to fathom. And it’s also very difficult and time-consuming to do a deep dive of your training data and analyse its learning value. But you really do need to do a thorough analysis if you want to improve the performance of your bot.
Here are three techniques: two to measure your bot’s performance and one for visualising the results of its performance.
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