Benchmarking Intent Classification services — June 2018

medium.com

Intent classification is an important component of any Natural Language Understanding (NLU) system in any chatbot platform. For the chatbot to work well, it must recognize correctly the intent of the user from user input in order to trigger the correct action or dialog. Another important aspect of NLU is entity extraction, which is the subject of a separate benchmark that we have completed previously. Usually in any bot-building platform, the bot developer creates a list of intents, and for each intent provides a set of training phrases representing what a normal user may say for that intent. The number of training phrases varies across intents and bots. We can expect from a few (<10) to a hundred and even more training phrases per intent. Once the bot is trained, the bot’s intent classification is evaluated using testing phrases to see if the bot detects the intents correctly.

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