DCN+: Mixed Objective and Deep Residual Coattention for Question Answering
This work addresses the problem of penalizing accurate but non-exact answers in question answering models, which is incremental as it builds on existing dynamic coattention networks.
The paper tackled the misalignment between evaluation metrics and optimization objectives in question answering by proposing a mixed objective combining cross entropy loss with self-critical policy learning, and improved dynamic coattention networks with a deep residual coattention encoder, achieving state-of-the-art results of 75.1% exact match accuracy and 83.1% F1 on the Stanford Question Answering Dataset.
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. Our proposals improve model performance across question types and input lengths, especially for long questions that requires the ability to capture long-term dependencies. On the Stanford Question Answering Dataset, our model achieves state-of-the-art results with 75.1% exact match accuracy and 83.1% F1, while the ensemble obtains 78.9% exact match accuracy and 86.0% F1.