DCN+: Mixed Objective and Deep Residual Coattention for Question Answering

Understand

Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally accurate.

• We propose a mixed objective that combines cross entropy loss with self-critical policy learning.
• The objective uses rewards derived from word overlap to solve the misalignment between evaluation metric and optimization objective.
• In addition to the mixed objective, we improve dynamic coattention networks (DCN) with a deep residual coattention encoder that is inspired by recent work in deep self-attention and residual networks.