Rationalizing Neural Predictions

Understand

Prediction without justification has limited applicability.

• As a remedy, we learn to extract pieces of input text as justifications -- rationales -- that are tailored to be short and coherent, yet sufficient for making the same prediction.
• Our approach combines two modular components, generator and encoder, which are trained to operate well together.
• The generator specifies a distribution over text fragments as candidate rationales and these are passed through the encoder for prediction.