Getting the Most out of Simile Recognition

• URL: http://arxiv.org/abs/2211.05984v1
• Date: Fri, 11 Nov 2022 03:22:45 GMT
• Title: Getting the Most out of Simile Recognition
• Authors: Xiaoyue Wang, Linfeng Song, Xin Liu, Chulun Zhou, Jinsong Su
• Abstract summary: Simile recognition involves two subtasks: simile sentence classification that discriminates whether a sentence contains simile, and simile component extraction that locates the corresponding objects. Recent work ignores features other than surface strings. We study two types of features: 1) input-side features that include POS tags, dependency trees and word definitions, and 2) decoding features that capture the interdependence among various decoding decisions.
• Score: 48.5838790615549
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simile recognition involves two subtasks: simile sentence classification that discriminates whether a sentence contains simile, and simile component extraction that locates the corresponding objects (i.e., tenors and vehicles). Recent work ignores features other than surface strings. In this paper, we explore expressive features for this task to achieve more effective data utilization. Particularly, we study two types of features: 1) input-side features that include POS tags, dependency trees and word definitions, and 2) decoding features that capture the interdependence among various decoding decisions. We further construct a model named HGSR, which merges the input-side features as a heterogeneous graph and leverages decoding features via distillation. Experiments show that HGSR significantly outperforms the current state-of-the-art systems and carefully designed baselines, verifying the effectiveness of introduced features. Our code is available at https://github.com/DeepLearnXMU/HGSR.

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