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

TL;DR

This paper introduces a novel question answering model that combines a mixed objective function with a deep residual coattention encoder, improving performance especially on long questions by better capturing dependencies.

Contribution

The paper proposes a mixed training objective and a deep residual coattention encoder to enhance question answering models, achieving state-of-the-art results on SQuAD.

Findings

Achieved 75.1% exact match accuracy on SQuAD.

Improved handling of long questions and dependencies.

Ensemble model reached 78.9% exact match accuracy.

Abstract

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.