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
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
Intent Detection
Diagnose and Fix
Measure the improvement
Bot Set Up
How do we set up your bot? +60% accuracy from day one
We build your training data based on
- Specifications of you NLU platform
- Language profile of your users: colloquial, formal…
We structure your intents based on
- Your intents, structured in ontology
- Your utterances, following linguistic parameters of your user profile
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
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
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…
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
Retrain the Bot
- Re-generate training and evaluation dataset
- Re-check linguistic profile and ontology structure
- Re-train NLU model with new dataset
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
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
Need More Info?
At Bitext, we focus on linguistic-based 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.
SAN FRANCISCO, USA
