This work focuses on answering single-relation factoid questions over
Freebase. Each question can acquire the answer from a single fact of form
(subject, predicate, object) in Freebase. This task, simple question answering
(SimpleQA), can be addressed via a two-step pipeline: entity linking and fact
selection. In fact selection, we match the subject entity in a fact candidate
with the entity mention in the question by a character-level convolutional
neural network (char-CNN), and match the predicate in that fact with the
question by a word-level CNN (word-CNN). This work makes two main
contributions. (i) A simple and effective entity linker over Freebase is
proposed. Our entity linker outperforms the state-of-the-art entity linker over
SimpleQA task. (ii) A novel attentive maxpooling is stacked over word-CNN, so
that the predicate representation can be matched with the predicate-focused
question representation more effectively. Experiments show that our system sets
new state-of-the-art in this task.

该研究针对 Freebase 中的单关系事实问题回答，提出了一种两步骤流水线方法解决问题：实体链接和事实选择，结果表明该方法在 SimpleQA 任务上超过了现有技术水平。