@inproceedings{marrese-taylor-etal-2018-iiidyt,
title = "{IIIDYT} at {S}em{E}val-2018 Task 3: Irony detection in {E}nglish tweets",
author = "Marrese-Taylor, Edison and
Ilic, Suzana and
Balazs, Jorge and
Prendinger, Helmut and
Matsuo, Yutaka",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1087",
doi = "10.18653/v1/S18-1087",
pages = "537--540",
abstract = "In this paper we introduce our system for the task of Irony detection in English tweets, a part of SemEval 2018. We propose representation learning approach that relies on a multi-layered bidirectional LSTM, without using external features that provide additional semantic information. Although our model is able to outperform the baseline in the validation set, our results show limited generalization power over the test set. Given the limited size of the dataset, we think the usage of more pre-training schemes would greatly improve the obtained results.",
}
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%0 Conference Proceedings
%T IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets
%A Marrese-Taylor, Edison
%A Ilic, Suzana
%A Balazs, Jorge
%A Prendinger, Helmut
%A Matsuo, Yutaka
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F marrese-taylor-etal-2018-iiidyt
%X In this paper we introduce our system for the task of Irony detection in English tweets, a part of SemEval 2018. We propose representation learning approach that relies on a multi-layered bidirectional LSTM, without using external features that provide additional semantic information. Although our model is able to outperform the baseline in the validation set, our results show limited generalization power over the test set. Given the limited size of the dataset, we think the usage of more pre-training schemes would greatly improve the obtained results.
%R 10.18653/v1/S18-1087
%U https://aclanthology.org/S18-1087
%U https://doi.org/10.18653/v1/S18-1087
%P 537-540
Markdown (Informal)
[IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets](https://aclanthology.org/S18-1087) (Marrese-Taylor et al., SemEval 2018)
ACL