  In this paper, we present a fast and strong neural approach for general
purpose text matching applications. We explore what is sufficient to build a
fast and well-performed text matching model and propose to keep three key
features available for inter-sequence alignment: original point-wise features,
previous aligned features, and contextual features while simplifying all the
remaining components. We conduct experiments on four well-studied benchmark
datasets across tasks of natural language inference, paraphrase identification
and answer selection. The performance of our model is on par with the
state-of-the-art on all datasets with much fewer parameters and the inference
speed is at least 6 times faster compared with similarly performed ones.
