What-if analysis on intent detection models and datasets
Research a method to visualize the impact of changes in the dataset for an intent detection solution impacts the performance of the trained intent detection model, without training the model.
- Responsible, explainable, and socially aware data science
- Human-centered and community-minded information systems
- Language processing
What do you get
- A challenging assignment within a practical environment
- € 1000 compensation, € 500 + lease car or € 600 + living space
- Professional guidance
- Courses aimed at your graduation period
- Support from our academic Research center at your disposal
- Two vacation days per month
What you will do
- 65% Research
- 10% Analyze, design, realize
- 25% Documentation
Intent detection models are used frequently to build chatbots, text analysis solutions, and knowledge mining solutions. There are great tools available to build a dataset for an intent detection model. However, these models don’t offer good tools to perform a what-if analysis on your intent detection dataset.
Users are often resorting to trial and error to improve the dataset for their intent detection. We want to research better methods of determining what data could have a positive effect on the performance of the intent detection model.
Ideally, we want to provide the user with an impact score for samples to let them understand the effect of adding a specific sample to the training dataset of the intent detection model.
Another interesting method of performing what-if analysis could be the application of model explainers so the user gets a clear understanding of what exactly is picked up from a sample that is added to the training dataset.
About Info Support Research Center
We anticipate on upcoming and future challenges and ensures our engineers develop cutting-edge solutions based on the latest scientific insights. Our research community proactively tackles emerging technologies. We do this in cooperation with renowned scientists, making sure that research teams are positioned and embedded throughout our organisation and our community, so that their insights are directly applied to our business. We truly believe in sharing knowledge, so we want to do this without any restrictions.
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