Bilingual Self-Attention Network: Generating Headlines for Online Linguistic Questions

Beginners of foreign languages tend to pose their linguistic questions online. To describe the confusion, they usually use several example sentences in a foreign language, which makes the linguistic problem tedious long, and composed of two different languages. Therefore, generating a brief headline that covers the main points of the linguist question is important. We propose a new task: Generating Headlines for online Linguistic Questions (GHLQ). The task differs from the previous headline generation task. The main point is that the linguistic question and the headline contain two languages with specific corresponding morphology and syntax. For this reason, we propose a bilingual self-attention network, which can jointly model the information in two different languages effectively. Besides, we construct two datasets, JapLQ and GerLQ, from the real world, and we conduct experiments on these datasets. By the ways of automatic evaluation and human evolution, results show the effectiveness of our model.