Frame-based Multi-level Semantics Representation for text matching

Text matching is a fundamental and critical problem in natural language understanding (NLU), where multi-level semantics matching is the most challenging task. Human beings can always leverage their semantic knowledge, while neural computer systems first learn sentence semantic representations and then perform text matching based on learned representation. However, without sufficient semantic information, computer systems will not perform very well. To bridge the gap, we propose a novel Frame-based Multi-level Semantics Representation (FMSR) model, which utilizes frame knowledge to extract multi-level semantic information within sentences explicitly for the text matching task. Specifically, different from existing methods that only rely on the sophisticated architectures, FMSR model, which leverages both frame and frame elements in FrameNet, is designed to integrate multi-level semantic information with attention mechanisms to learn better sentence representations. Our extensive experimental results show that FMSR model performs better than the state-of-the-art technologies on two text matching tasks. (C) 2021 Published by Elsevier B.V.

Automated summary

This paper introduces a Frame-based Multi-level Semantics Representation (FMSR) model, which utilizes frame knowledge and attention mechanisms to extract multi-level semantic information within sentences for text matching tasks. Experimental results show that the FMSR model outperforms state-of-the-art technologies in text matching tasks.