Simple and Effective Text Matching with Richer Alignment Features
TL
TL;DR
A fast and strong neural approach for general purpose text matching applications and proposes 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.
Abstract
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.
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Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences
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Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information
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