@inproceedings{wu-etal-2018-thu,
title = "{THU}{\_}{NGN} at {S}em{E}val-2018 Task 3: Tweet Irony Detection with Densely connected {LSTM} and Multi-task Learning",
author = "Wu, Chuhan and
Wu, Fangzhao and
Wu, Sixing and
Liu, Junxin and
Yuan, Zhigang and
Huang, Yongfeng",
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/S18-1006",
doi = "10.18653/v1/S18-1006",
pages = "51--56",
abstract = "Detecting irony is an important task to mine fine-grained information from social web messages. Therefore, the Semeval-2018 task 3 is aimed to detect the ironic tweets (subtask A) and their ironic types (subtask B). In order to address this task, we propose a system based on a densely connected LSTM network with multi-task learning strategy. In our dense LSTM model, each layer will take all outputs from previous layers as input. The last LSTM layer will output the hidden representations of texts, and they will be used in three classification task. In addition, we incorporate several types of features to improve the model performance. Our model achieved an F-score of 70.54 (ranked 2/43) in the subtask A and 49.47 (ranked 3/29) in the subtask B. The experimental results validate the effectiveness of our system.",
}
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%0 Conference Proceedings
%T THU_NGN at SemEval-2018 Task 3: Tweet Irony Detection with Densely connected LSTM and Multi-task Learning
%A Wu, Chuhan
%A Wu, Fangzhao
%A Wu, Sixing
%A Liu, Junxin
%A Yuan, Zhigang
%A Huang, Yongfeng
%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 wu-etal-2018-thu
%X Detecting irony is an important task to mine fine-grained information from social web messages. Therefore, the Semeval-2018 task 3 is aimed to detect the ironic tweets (subtask A) and their ironic types (subtask B). In order to address this task, we propose a system based on a densely connected LSTM network with multi-task learning strategy. In our dense LSTM model, each layer will take all outputs from previous layers as input. The last LSTM layer will output the hidden representations of texts, and they will be used in three classification task. In addition, we incorporate several types of features to improve the model performance. Our model achieved an F-score of 70.54 (ranked 2/43) in the subtask A and 49.47 (ranked 3/29) in the subtask B. The experimental results validate the effectiveness of our system.
%R 10.18653/v1/S18-1006
%U https://aclanthology.org/S18-1006
%U https://doi.org/10.18653/v1/S18-1006
%P 51-56
Markdown (Informal)
[THU_NGN at SemEval-2018 Task 3: Tweet Irony Detection with Densely connected LSTM and Multi-task Learning](https://aclanthology.org/S18-1006) (Wu et al., SemEval 2018)
ACL
