How we made TechCrunch's bot more conversational

Some of you have asked for more details about the TechCrunch's Messenger bot upgrade, so we decided to share them here. Thus, in this article we will disclosure what exactly did the improvement consist on, along with some query examples and how this evolution to a more conversational bot was possible.

TechCrunch launched their bot to deliver news and for users to be able to ask for new stories on a certain topic. It was able to attend basic requests, like asking for the main menu, managing subscriptions or giving feedback. Also, users could write down the name of any news section in the web to get the latest stories featured in it.

But all these interactions with the bot were limited to certain strings: you had to type exactly “main menu”, “about”, “manage subscriptions”, etc. So users had to remember the precise phrase, its words and order, and couldn’t get out of that.

What was the improvement about?

With Bitext technology, some advanced features were added to the bot’s Natural Language Understanding (NLU) system, allowing it to correctly interpret three new types of queries:

  • simple intent queries expressed in natural language
  • double intent queries
  • negated queries

This operation is seamless and doesn’t interfere with previous features of the bot, as can be seen in the table below.

Query examples that show the improvement in NLU of TechCrunch's bot

Try TechCrunch bot

How was this transformation into a more conversational bot possible?

Maybe you are thinking TechCrunch’s bot was trained with large amounts of data and that it involved a high computing power, but that’s not what happened. The solution was to integrate the Bitext Query Rewriting technology, which simplifies users’ queries and transforms them to be easily understood by trivially trained bots.

The pipeline works like this:

  1. First, any query sent by a user goes directly through the Query Rewriting middleware service.
  2. The Query Rewriting service simplifies the sentence, removing the non-important parts and generating a simpler, canonical version of the original query. This rewritten sentence is sent to the intent detection engine so the chatbot can figure out what the user is asking for. That way, the bot had to be trained only with a small amount of simplified queries like this one to be able to understand all kinds of natural language queries (NLQ).
  3. Once the intent is detected, Bitext once again rewrites the sentence into a boolean string, adding commands such as “AND” or “OR”. This step is key, so when the basic phrase including booleans goes to the search engine, the retrieval answer is the correct one.
  4. Finally, the search engine delivers the results, the ones that match the boolean query received from the previous step, to the bot user.

As you can see, the whole architecture is completely modular, and Bitext NLP middleware just acts as a convenient NLP layer to boost the chatbot performance.

If you want to see a whole comparative of TechCrunch's bot before and after the upgrade and more queries and details about how it was done, you can download the benchmark below.

Download benchmark about TechCrunch's bot upgrade

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