Getting the Most out of Simile Recognition (2022)

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 and suffers from the data hunger issue.We explore expressive features for this task to help achieve more effective data utilization.In particular, 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. We will release our code upon paper acceptance.

## introduction simile is a type of figurative that compares two objects (named tenor and vehicle) of different categories using comparator words such as "like", "as" or "than". ## problem formulation formally, given an input sentence @xmath0, . . . , w i , . . . , w n containing a comparator w c , the goal is to detect whether the comparator w c is a simile and what spans in s correspond to the simile components (tensors and vehicles). note that a comparator may correspond to a literal comparison (rather than a simile), such as "the sheep looks like an australian sheep breed." following previous work @xcite @xcite , we formulate the two subtasks as binary classification and sequence labeling, respectively. ## baseline: bert for simile recognition in this section, we introduce a baseline for simile recognition based on bert @xcite , temred as bsr. ## model in this section, we give more details of our model that takes input-side features ( §4.1) and decoding feature ( §4.2) for simile recognition. for fair comparison, our model is mostly consistent with the baseline ( §3) with slight changes (shown below) for incorporating our introduced features. ## experiments we conduct detailed experiments and analysis to investigate the effectiveness of our model. ## related work our related work mainly includes the studies of simile recognition and heterogeneous neural network for nlp. ## conclusion in this paper, we propose hgsr, which gets the most out of task features to alleviate the data hunger issue for simile recognition. concretely, we explore the input-side features and the decoding fea- type & cnt example cyc-mtl hgsr component dropping (137/77) 一篇篇作文、日记像泉水一样从笔下涌出。 pieces of essays and diaries gush out from the writing like spring. type: simile tenor: 日记(diaries) vehicle: 泉水(spring) type: simile tenor: 日记(diaries) tenor: 作文(essays) vehicle: 泉水(spring) locating error (86/57) 乌龟的壳像小山。 the shell of the tortoise is like a hill. type: simile tenor: 乌龟(tortoise) vehicle: 小山(hill) type: simile tenor: 壳(shell) vehicle: 小山(hill) simile classification error (197/156) 如果不保护动物，大熊猫迟早会像恐龙一样 灭绝。 if animals are not protected, pandas will become extinct like dinosaurs sooner or later. type: simile tenor: 熊猫(pandas) vehicle: 恐龙(dinosaurs) type: literal redundant component extraction (15/18) 天空中没有星星，它像一个巨大的黑洞。 there are no stars in the sky, it likes a huge black hole. type: simile tenor: 它(it) vehicle: 星星(stars) type: simile tenor: 天空(sky) tenor: 它(it) vehicle:黑洞(black hole)