user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DCN+: Mixed Objective And Deep Residual Coattention for Question Answering

Paper content:
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, using rewards derived from word overlap to solve the misalignment between evaluation metric and optimization objective.
In addition to the mixed objective, we introduce 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.
Existing state-of-the-art question answering models are trained to produce exact answer spans for a question and a document.
In this setting, a ground truth answer used to supervise the model is defined as a start and an end position within the document.
Existing training approaches optimize using cross entropy loss over the two positions.
However, this suffers from a fundamental disconnect between the optimization, which is tied to the position of a particular ground truth answer span, and the evaluation, which is based on the textual content of the answer.
This disconnect is especially harmful in cases where answers that are textually similar to, but distinct in positions from, the ground truth are penalized in the same fashion as answers that are textually dissimilar.
For example, suppose we are given the sentence "Some believe that the Golden State Warriors team of 2017 is one of the greatest teams in NBA history", the question "which team is considered to be one of the greatest teams in NBA history", and a ground truth answer of "the Golden State Warriors team of 2017".
The span "Warriors" is also a correct answer, but from the perspective of traditional cross entropy based training it is no better than the span "history".To
address this problem, we propose a mixed objective that combines traditional cross entropy loss over positions with a measure of word overlap trained with reinforcement learning. We
obtain the latter objective using self-critical policy learning in which the reward is based on word overlap between the proposed answer and the ground truth answer. Our
mixed objective brings two benefits: (i)
the reinforcement learning objective encourages answers that are textually similar to the ground truth answer and discourages those that are not; (ii
) the cross entropy objective significantly facilitates policy learning by encouraging trajectories that are known to be correct. The
resulting objective is one that is both faithful to the evaluation metric and converges quickly in practice.In addition to our mixed training objective, we extend the Dynamic Coattention Network (DCN) by with a deep residual coattention encoder. This
allows the network to build richer representations of the input by enabling each input sequence to attend to previous attention contexts. BID26
show that the stacking of attention layers helps model long-range DISPLAYFORM0 Figure 1: Deep residual coattention encoder.dependencies. We merge
coattention outputs from each layer by means of residual connections to reduce the length of signal paths. BID6 show
that skip layer connections facilitate signal propagation and alleviate gradient degradation.The combination of the deep residual coattention encoder and the mixed objective leads to higher performance across question types, question lengths, and answer lengths on the Stanford Question Answering Dataset (SQuAD) BID20 compared to our DCN baseline. The improvement
is especially apparent on long questions, which require the model to capture long-range dependencies between the document and the question. Our model, which
we call DCN+, achieves state-of-the-art results on SQuAD, with 75.1% exact match accuracy and 83.1% F1. When ensembled,
the DCN+ obtains 78.9% exact match accuracy and 86.0% F1.
We introduced DCN+, an state-of-the-art question answering model with deep residual coattention trained using a mixed objective that combines cross entropy supervision with self-critical policy learning.
We showed that our proposals improve model performance across question types, question lengths, and answer lengths on the Stanford Question Answering Dataset ( SQuAD).
On SQuAD, the DCN+ achieves 75.1% exact match accuracy and 83.1% F1.
When ensembled, the DCN+ obtains 78.9% exact match accuracy and 86.0% F1.

assistant: text: We introduce the DCN+ with deep residual coattention and mixed-objective RL, which achieves state of the art performance on the Stanford Question Answering Dataset.