论文标题
Dyrex：提取问题回答的动态查询表示
DyREx: Dynamic Query Representation for Extractive Question Answering
论文作者
论文摘要
提取问题回答（EXQA）是自然语言处理的重要任务。 EXQA的主要方法是用预训练的变压器代表输入序列令牌（问题和段落），然后使用两个学习的查询向量在开始和最终答案跨度位置上计算分布。这些查询向量缺乏输入的上下文，这可能是模型性能的瓶颈。为了解决这个问题，我们提出了\ textit {dyrex}，这是\ textit {vanilla}方法的概括，在该方法中，我们使用通过变压器层的注意机制动态计算给定输入的查询向量。经验观察表明，我们的方法一致地改善了标准的性能。运行实验的代码和随附的文件可在\ url {https://github.com/urchade/dyrex}上获得。
Extractive question answering (ExQA) is an essential task for Natural Language Processing. The dominant approach to ExQA is one that represents the input sequence tokens (question and passage) with a pre-trained transformer, then uses two learned query vectors to compute distributions over the start and end answer span positions. These query vectors lack the context of the inputs, which can be a bottleneck for the model performance. To address this problem, we propose \textit{DyREx}, a generalization of the \textit{vanilla} approach where we dynamically compute query vectors given the input, using an attention mechanism through transformer layers. Empirical observations demonstrate that our approach consistently improves the performance over the standard one. The code and accompanying files for running the experiments are available at \url{https://github.com/urchade/DyReX}.
