Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension. We present a fine-grained gating mechanism to dynamically combine word-level and character-level representations based on properties of the words. We also extend the idea of fine-grained gating to modeling the interaction between questions and paragraphs for reading comprehension. Experiments show that our approach can improve the performance on reading comprehension tasks, achieving new state-of-the-art results on the Children's Book Test dataset. To demonstrate the generality of our gating mechanism, we also show improved results on a social media tag prediction task.

本文提出了细粒度的门控机制来动态地组合基于单词属性的单词级别和字符级别表示，以提高阅读理解任务的性能，并将细粒度门控的思想扩展到建立问题和段落之间的交互模型。实验结果显示，我们的方法可以在阅读理解任务中提高性能，获得儿童读物测试数据集的最新结果。此外，我们还展示了在社交媒体标签预测任务上的改善结果证明了我们的门控机制的通用性。

词还是字？用于阅读理解的细粒度门控