A Compare-Aggregate Model with Embedding Selector for Answer Selection

Abstract

Answer selection is a challenging task in natural language processing that requires both natural language understanding and word knowledge. At present, most of recent methods draw on insights from attention mechanism to learn the complex semantic relations between questions and answers. Previous remarkable approaches mainly apply general Compare-Aggregate framework. In this paper, we propose a novel Compare-Aggregate framework with embedding selector to solve answer selection task. Unlike previous Compare-Aggregate methods which just use one type of Attention mechanism and lack the use of word vectors at different level, we employ two types of Attention mechanism in a model and add a selector layer to choose a best input for aggregation layer. We evaluate the model on the two answer selection tasks: WikiQA and TrecQA. On the two different datasets, our approach outperforms several strong baselines and achieves state-of-the-art performance.