Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information

TL;DR

This paper introduces a densely-connected co-attentive recurrent neural network for sentence matching, which preserves original features through dense concatenation and autoencoding, achieving state-of-the-art results on benchmark datasets.

Contribution

It proposes a novel densely-connected co-attentive RNN architecture with feature preservation and autoencoding, improving sentence matching performance.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Effectively preserves original and attentive features across layers.

Outperforms previous attention-based methods in sentence matching tasks.

Abstract

Sentence matching is widely used in various natural language tasks such as natural language inference, paraphrase identification, and question answering. For these tasks, understanding logical and semantic relationship between two sentences is required but it is yet challenging. Although attention mechanism is useful to capture the semantic relationship and to properly align the elements of two sentences, previous methods of attention mechanism simply use a summation operation which does not retain original features enough. Inspired by DenseNet, a densely connected convolutional network, we propose a densely-connected co-attentive recurrent neural network, each layer of which uses concatenated information of attentive features as well as hidden features of all the preceding recurrent layers. It enables preserving the original and the co-attentive feature information from the bottommost word embedding layer to the uppermost recurrent layer. To alleviate the problem of an ever-increasing size of feature vectors due to dense concatenation operations, we also propose to use an autoencoder after dense concatenation. We evaluate our proposed architecture on highly competitive benchmark datasets related to sentence matching. Experimental results show that our architecture, which retains recurrent and attentive features, achieves state-of-the-art performances for most of the tasks.