# Getting the Most out of Simile Recognition

arXiv: https://arxiv.org/abs/2211.05984 · HTML: https://mlsanity.com/paper/2211.05984/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 32 references that are in the map.
- First posted on arXiv 11 Nov 2022.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://mlsanity.com/paper/1809.10185/) (2018)
- [Heterogeneous Graph Neural Networks for Extractive Document Summarization](https://mlsanity.com/paper/2004.12393/) (2020)
- [Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs](https://mlsanity.com/paper/1905.07374/) (2019)
- [A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation](https://mlsanity.com/paper/2007.08742/) (2020)
- [Graph-based Neural Sentence Ordering](https://mlsanity.com/paper/1912.07225/) (2019)
- [Neural Simile Recognition with Cyclic Multitask Learning and Local Attention](https://mlsanity.com/paper/1912.09084/) (2019)
- [A Label Dependence-aware Sequence Generation Model for Multi-level Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2112.11740/) (2021)
- [Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU](https://mlsanity.com/paper/1910.00912/) (2019)
- [Cross-lingual Text Classification with Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2105.11246/) (2021)
- [Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories](https://mlsanity.com/paper/2110.14091/) (2021)
- [Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings](https://mlsanity.com/paper/2110.06446/) (2021)

## Built on by (most cited first, 2 of 2)

- [CERD: A Comprehensive Chinese Rhetoric Dataset for Rhetorical Understanding and Generation in Essays](https://mlsanity.com/paper/2409.19691/) (2024)
- [A Dual-Perspective Metaphor Detection Framework Using Large Language Models](https://mlsanity.com/paper/2412.17332/) (2024)

## Abstract (arXiv)

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.