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We Make Conversational Bots Work, Using Synthetic Data for Intent Detection

We automate the training and evaluation process to increase your Intent Detection accuracy, completion rate and decrease your churn. 

We Make Conversational Bots Work, Using Synthetic Data for Intent Detection

We automate the training and evaluation process to increase your Intent Detection accuracy, completion rate and decrease your churn.

 

Working with 3 of the Top 5 largest companies in NASDAQ

Working with 3 of the Top 5 largest companies in NASDAQ

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We run all data-related issues: creation, tagging and overlapping

 

  • We commit to performance metrics: accuracy, deflection…
  • We automate (re-)training and (re-)evaluation
  • We take care of all your data problems
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    Intent Detection

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    Diagnose and Fix

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    Measure the improvement

    Bot Set Up

    How do we set up your bot? +60% accuracy from day one

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    We build your training data based on

    • Specifications of you NLU platform
    • Language profile of your users: colloquial, formal…
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    We structure your intents based on

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    We generate training and evaluation data

    • To train and evaluate your bot
    • At scale, using our proprietary NLG

    Bot Improvement

    How to evaluate and improve your bot to achieve 90% accuracy?

    The 6 essential steps in the life of your bot

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    Evaluate Bot Performance

     

    • Select a GOLD STANDARD based on a few thousand user queries
    • Annotate it manually, that’s your “ground truth”
    • COMPARE BOT AND MANUAL ANNOTATION to IDENTIFY BOT ERRORS
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    Diagnose Bot Errors

     

     

    • Identify intents AFFECTED BY COMMON ERRORS
    • Identify INTENTS THAT OVERLAP, the ones that cause more troble
    • Typical error sources: synonyms, language register, transcription errors…
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    Define a Strategy to fix Common Errors

     

    • DEFINE DATA NEEDS: linguistic phenomena poorly covered, new topics not considered in intent design…
    • Update NLG parameters to fix the errors with the highest impact
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    Retrain the Bot

     

     

     

    • Re-generate training and evaluation dataset
    • Re-check linguistic profile and ontology structure
    • Re-train NLU model with new dataset
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    Re-evaluate Bot accuracy in two steps

     

     

    • First, Internal Evaluation: data consistency, k-fold cross-validation and semantic coverage
    • Second, External Evaluation: real user data
    • Then, execute regression test to validate improvement
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    Measure Progress and Consolidate Improvements

     

    • Check that COMMON ERRORS ARE FIXED
    • Check that things that were working “did not get broken”
    • CONSOLIDATE FIXES AND LAUNCH new version

    NLP Solutions for AI

    Building an NLP engine requires deep technical knowledge to make it work

    Natural Language is not a piece of cake. Processing the way people talk is a much more complex science than it seems and requires highly specialized resources. This leads to an expensive and laborious procedure in order to get AI ready to be used.

     

    NLP Services

    • Improve your model with better word embeddings
    • Run your NLP engine on your device
    • Make your NLP engine understand up to 50 languages
    • Reduce training time of your Machine Learning models
    • Improve your bot’s understanding skills
    • Ready to go and easy to implement NLP engine

     

    Train your bot on any platform

    Multilingual Approach

    More than 100 languages and variants available

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    Benchmark on Amazon Lex

    Check out how we improved Amazon Lex accuracy by 50% using our training data

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    Benchmark on Microsoft LUIS

    Increase accuracy on the LUIS platform up to 40% using Bitext training data

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    Benchmark on Dialogflow

    A benchmark based on Dialogflow shows accuracy increases of up to 40%

    Bitext Word Embeddings

    Benchmark on Entity Extraction

    This report compares Bitext’s entity extraction software to 3 other engines (CRFSuite, Stanford and SENNA)

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    Benchmark on Lemmatization

    A brief comparison of stemmers and lemmatizers

    Benchmark on Amazon Lex

    Check out how we improved Amazon Lex accuracy by 50% using our training data

    Benchmark on Microsoft LUIS

    Increase accuracy on the LUIS platform up to 40% using Bitext training data

    Benchmark on Dialogflow

    A benchmark based on Dialogflow shows accuracy increases of up to 40%

    Worldwide Language Coverage

    Would you like to get more info?

    At Bitext, we provide a clear emphasis on linguistic-based abstraction language automation to deliver innovative customer experiences. If you want to test our solutions or learn more, we recommend you schedule a personalized demo from one of our experts or start using our API. You have a 30 days free trial.

    Request a Demo

    Would you like to get more info?

    At Bitext, we provide a clear emphasis on linguistic-based abstraction language automation to deliver innovative customer experiences.  If you want to test our solutions or learn more, we recommend you to get a personalized demo from one of our experts or start using our API. You have a 30 days free trial.

     

    Request a Demo

    SAN FRANCISCO, USA

    541 Jefferson Ave., Ste. 100

    Redwood City

    CA 94063

    MADRID, SPAIN

    José Echegaray 8, Building 3

    Parque Empresarial Las Rozas

    28232 Las Rozas