Ranking via partial ordering for answer selection

Answer selection with deep neural networks has been studied extensively owing to its strong capability of encoding semantic features. Most previous work has treated the candidates as independent individuals for ranking but ignored the relations between these. In this study, we propose a ranking method via partial ordering for answer selection, which is applicable to various tasks, such as question answering and reading comprehension. First, we propose a comparative network, namely Candidate vs. Candidate, which aims to discover the possible partial order relations between (candidate, candidate) pairs. Thereafter, a multi-task learning framework is constructed for the answer selection problem, in which the main task is representing the relevance between (question, answer) pairs, while the auxiliary sub-task is learning the partial order relations between (answer, answer) pairs. By jointly training the networks with abundant supervision information, a reasonable relevance function and a comparison function can be approximated for these tasks. The experimental results on four benchmarks indicate that ranking candidate answers via partial ordering can significantly improve the answer selection performance.