Can you trust your model when data shifts?
In real-case scenarios NLP solutions need to deal with noisy data, and one of the most common sources of noise is “time”. Data distributions can really change a lot through time! Thus we decided to test “robustness to data shift” and compare end-2-end data-driven machine learning solutions with a human-driven symbolic approach. Let’s see what … Continued