Common problem:

Your bot doesn't understand and behaves like a robot

Machine learning algorithms, whatever their applications, require a lot of data to learn, and the data has to be tagged for each specific purpose. Typically, this data generation and tagging are done manually. However, this approach has three negative consequences when we talk about creating bots:

  • It consumes a lot of resources: time and human effort

  • If data is scarce, the bot understanding will be poor

  • Users are forced to talk like robots to be understood, so they will never engage with bots, and therefore, they will be dead


Make your bot intelligent and conversational

Bitext NLP middleware for bot training offers you the most flexible solution in the market to enhance the communication between humans and machines. By implementing our technology inside your bot, it will be able to understand users’ requests without forcing them to speak like robots and avoiding “I did not understand” replies.

Bitext NLP middleware for bots can be used with every major bot training platform like Dialogflow,, LUIS, Lex, Watson, and others.
Our technology can automatically train a bot developed under Dialogflow and achieve 100% accuracy compared to the 37% accuracy achieved by Dialogflow alone.

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Query Simplification:

A featured service of Bitext NLP middleware

We rewrite user queries in order to simplify them, to make them easier to understand for you bot. We take a complex querie like Can you turn on the lights in kitchen, please? and rewrite it as Turn on lights in kitchen. It reduces different user requests to a normalized form that captures the common meaning of all variants.

Our solution also allows to solve one huge problem in the industry: coordinated sentences (aka double intent or multiple actions): we know the inner workings and the biggest needs of AI systems, so if a user query contains a coordination at any level the Query Rewriting service will detect it and split the result, making it easier to handle. See examples of coordination at action level and object level below:

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Query Simplification Case Study: TechCrunch's News Bot

Using the Query Simplification service, TechCrunch was able to make their news bot smarter and more conversational. It turned from a very simple NLU system to handle natural language queries, double intent and conversational negation. Check out how it was built:

TechCrunch logo

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Query Simplification + Negation:

Query Simplification to its full potential

Solve one of the biggest issues in NLU: negation. Has it happened to you that you give a command that includes a negated part and the results given to your search or question just ignore that negation? Natural language is rich in its expressiveness and we cannot ask users to change the way they communicate. However, detecting negation has not yet been popularized and leaves the client rather frustrated.

But now, thanks to the linguistic knowledge all Bitext technologies are based on, negation at any level is recognized, conveniently flagged, and returned in a structure that is understandable for any bot.

sentence simplification

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