@inproceedings{cignarella-etal-2020-multilingual,
title = "Multilingual Irony Detection with Dependency Syntax and Neural Models",
author = "Cignarella, Alessandra Teresa and
Basile, Valerio and
Sanguinetti, Manuela and
Bosco, Cristina and
Rosso, Paolo and
Benamara, Farah",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.116",
doi = "10.18653/v1/2020.coling-main.116",
pages = "1346--1358",
abstract = "This paper presents an in-depth investigation of the effectiveness of dependency-based syntactic features on the irony detection task in a multilingual perspective (English, Spanish, French and Italian). It focuses on the contribution from syntactic knowledge, exploiting linguistic resources where syntax is annotated according to the Universal Dependencies scheme. Three distinct experimental settings are provided. In the first, a variety of syntactic dependency-based features combined with classical machine learning classifiers are explored. In the second scenario, two well-known types of word embeddings are trained on parsed data and tested against gold standard datasets. In the third setting, dependency-based syntactic features are combined into the Multilingual BERT architecture. The results suggest that fine-grained dependency-based syntactic information is informative for the detection of irony.",
}
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%0 Conference Proceedings
%T Multilingual Irony Detection with Dependency Syntax and Neural Models
%A Cignarella, Alessandra Teresa
%A Basile, Valerio
%A Sanguinetti, Manuela
%A Bosco, Cristina
%A Rosso, Paolo
%A Benamara, Farah
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F cignarella-etal-2020-multilingual
%X This paper presents an in-depth investigation of the effectiveness of dependency-based syntactic features on the irony detection task in a multilingual perspective (English, Spanish, French and Italian). It focuses on the contribution from syntactic knowledge, exploiting linguistic resources where syntax is annotated according to the Universal Dependencies scheme. Three distinct experimental settings are provided. In the first, a variety of syntactic dependency-based features combined with classical machine learning classifiers are explored. In the second scenario, two well-known types of word embeddings are trained on parsed data and tested against gold standard datasets. In the third setting, dependency-based syntactic features are combined into the Multilingual BERT architecture. The results suggest that fine-grained dependency-based syntactic information is informative for the detection of irony.
%R 10.18653/v1/2020.coling-main.116
%U https://aclanthology.org/2020.coling-main.116
%U https://doi.org/10.18653/v1/2020.coling-main.116
%P 1346-1358
Markdown (Informal)
[Multilingual Irony Detection with Dependency Syntax and Neural Models](https://aclanthology.org/2020.coling-main.116) (Cignarella et al., COLING 2020)
ACL
- Alessandra Teresa Cignarella, Valerio Basile, Manuela Sanguinetti, Cristina Bosco, Paolo Rosso, and Farah Benamara. 2020. Multilingual Irony Detection with Dependency Syntax and Neural Models. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1346–1358, Barcelona, Spain (Online). International Committee on Computational Linguistics.