Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance

Understand

Models for natural language understanding (NLU) tasks often rely on the idiosyncratic biases of the dataset, which make them brittle against test cases outside the training distribution.

• Recently, several proposed debiasing methods are shown to be very effective in improving out-of-distribution performance.
• However, their improvements come at the expense of performance drop when models are evaluated on the in-distribution data, which contain examples with higher diversity.
• This seemingly inevitable trade-off may not tell us much about the changes in the reasoning and understanding capabilities of the resulting models on broader types of examples beyond the small subset represented in the out-of-distribution data.