  Answer selection and knowledge base question answering (KBQA) are two
important tasks of question answering (QA) systems. Existing methods solve
these two tasks separately, which requires large number of repetitive work and
neglects the rich correlation information between tasks. In this paper, we
tackle answer selection and KBQA tasks simultaneously via multi-task learning
(MTL), motivated by the following motivations. First, both answer selection and
KBQA can be regarded as a ranking problem, with one at text-level while the
other at knowledge-level. Second, these two tasks can benefit each other:
answer selection can incorporate the external knowledge from knowledge base
(KB), while KBQA can be improved by learning contextual information from answer
selection. To fulfill the goal of jointly learning these two tasks, we propose
a novel multi-task learning scheme that utilizes multi-view attention learned
from various perspectives to enable these tasks to interact with each other as
well as learn more comprehensive sentence representations. The experiments
conducted on several real-world datasets demonstrate the effectiveness of the
proposed method, and the performance of answer selection and KBQA is improved.
Also, the multi-view attention scheme is proved to be effective in assembling
attentive information from different representational perspectives.
