# Neural Simile Recognition with Cyclic Multitask Learning and Local Attention

arXiv: https://arxiv.org/abs/1912.09084 · HTML: https://mlsanity.com/paper/1912.09084/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2019.

## Built on by (most cited first, 18 of 18)

- [Multi-Task Learning in Natural Language Processing: An Overview](https://mlsanity.com/paper/2109.09138/) (2021)
- [It's not Rocket Science : Interpreting Figurative Language in Narratives](https://mlsanity.com/paper/2109.00087/) (2021)
- [IRFL: Image Recognition of Figurative Language](https://mlsanity.com/paper/2303.15445/) (2023)
- [Jawaher: A Multidialectal Dataset of Arabic Proverbs for LLM Benchmarking](https://mlsanity.com/paper/2503.00231/) (2025)
- [ePiC: Employing Proverbs in Context as a Benchmark for Abstract Language Understanding](https://mlsanity.com/paper/2109.06838/) (2021)
- [Can Pre-trained Language Models Interpret Similes as Smart as Human?](https://mlsanity.com/paper/2203.08452/) (2022)
- [Nominal Metaphor Generation with Multitask Learning](https://mlsanity.com/paper/2206.05195/) (2022)
- [Enhanced Aspect-Based Sentiment Analysis Models with Progressive Self-supervised Attention Learning](https://mlsanity.com/paper/2103.03446/) (2021)
- [Probing Simile Knowledge from Pre-trained Language Models](https://mlsanity.com/paper/2204.12807/) (2022)
- [When Meaning Isn't Literal: Exploring Idiomatic Meaning Across Languages and Modalities](https://mlsanity.com/paper/2604.10787/) (2026)
- [ViPE: Visualise Pretty-much Everything](https://mlsanity.com/paper/2310.10543/) (2023)
- [Getting the Most out of Simile Recognition](https://mlsanity.com/paper/2211.05984/) (2022)
- [MAPS-KB: A Million-scale Probabilistic Simile Knowledge Base](https://mlsanity.com/paper/2212.05254/) (2022)
- [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)
- [I-WAS: a Data Augmentation Method with GPT-2 for Simile Detection](https://mlsanity.com/paper/2308.04109/) (2023)
- [A Survey of Text Style Transfer: Applications and Ethical Implications](https://mlsanity.com/paper/2407.16737/) (2024)
- [When Meaning Travels: A Granular Lens on Hybrid-MoE's Role in Idiomatic Understanding for Language Models](https://mlsanity.com/paper/2606.01671/) (2026)

## Abstract (arXiv)

Simile recognition is to detect simile sentences and to extract simile components, i.e., tenors and vehicles. It involves two subtasks: it simile sentence classification and it simile component extraction. Recent work has shown that standard multitask learning is effective for Chinese simile recognition, but it is still uncertain whether the mutual effects between the subtasks have been well captured by simple parameter sharing. We propose a novel cyclic multitask learning framework for neural simile recognition, which stacks the subtasks and makes them into a loop by connecting the last to the first. It iteratively performs each subtask, taking the outputs of the previous subtask as additional inputs to the current one, so that the interdependence between the subtasks can be better explored. Extensive experiments show that our framework significantly outperforms the current state-of-the-art model and our carefully designed baselines, and the gains are still remarkable using BERT.