Research Area: Machine Learning
Sentence semantic matching (SSM) always plays a critical role in natural language processing. Measuring the intrinsic semantic similarity among sentences is very challenging and has not been substantially addressed. The latest SSM research usually relies on a shallow text representation and interaction between sentence pairs, which might not be enough to capture the complex semantic features and lead to limited performance. To capture more semantic context features and interactions, we propose a hierarchical encoding model (HEM) for sentence representation, further enhanced by a hierarchical matching mechanism for sentence interaction. Given two sentences, HEM generates intermediate and final representations in encoding layer, which are further handled by a novel hierarchical matching mechanism to capture more multi-view interactions in matching layer. The comprehensive experiments demonstrate that our model is capable to capture more sentence semantic features and interactions, which significantly outperforms the existing state-of-the-art neural models on the public real-world dataset.
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Author(s) Name: Wenpeng Lu, Xu Zhang, Huimin Lu, Fangfang Li
Journal name: Journal of Visual Communication and Image Representation
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.jvcir.2020.102794
Volume Information: Volume 71, August 2020, 102794
