@inproceedings{wang-etal-2022-getting,
title = "Getting the Most out of Simile Recognition",
author = "Wang, Xiaoyue and
Song, Linfeng and
Liu, Xin and
Zhou, Chulun and
Zeng, Hualin and
Su, Jinsong",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.236",
doi = "10.18653/v1/2022.findings-emnlp.236",
pages = "3243--3252",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Getting the Most out of Simile Recognition
%A Wang, Xiaoyue
%A Song, Linfeng
%A Liu, Xin
%A Zhou, Chulun
%A Zeng, Hualin
%A Su, Jinsong
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F wang-etal-2022-getting
%X 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.
%R 10.18653/v1/2022.findings-emnlp.236
%U https://aclanthology.org/2022.findings-emnlp.236
%U https://doi.org/10.18653/v1/2022.findings-emnlp.236
%P 3243-3252
Markdown (Informal)
[Getting the Most out of Simile Recognition](https://aclanthology.org/2022.findings-emnlp.236) (Wang et al., Findings 2022)
ACL
- Xiaoyue Wang, Linfeng Song, Xin Liu, Chulun Zhou, Hualin Zeng, and Jinsong Su. 2022. Getting the Most out of Simile Recognition. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3243–3252, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.