Q: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
This work focuses on answering singlerelation 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 twostep pipeline: entity linking and fact selection. In fact selection, we match the subject entity in fact with the entity mention in question by a character-level convolutional neural network (char-CNN), and match the predicate in fact with the question by a word-level CNN (wordCNN). 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 of 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.
A:
Simple Question Answering by Attentive Convolutional Neural Network