@inproceedings{gee-wang-2018-psyml,
title = "psy{ML} at {S}em{E}val-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis",
author = "Gee, Grace and
Wang, Eugene",
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/S18-1056",
doi = "10.18653/v1/S18-1056",
pages = "369--376",
abstract = "In this paper, we describe the first attempt to perform transfer learning from sentiment to emotions. Our system employs Long Short-Term Memory (LSTM) networks, including bidirectional LSTM (biLSTM) and LSTM with attention mechanism. We perform transfer learning by first pre-training the LSTM networks on sentiment data before concatenating the penultimate layers of these networks into a single vector as input to new dense layers. For the E-c subtask, we utilize a novel approach to train models for correlated emotion classes. Our system performs 4/48, 3/39, 8/38, 4/37, 4/35 on all English subtasks EI-reg, EI-oc, V-reg, V-oc, E-c of SemEval 2018 Task 1: Affect in Tweets.",
}
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%0 Conference Proceedings
%T psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis
%A Gee, Grace
%A Wang, Eugene
%S Proceedings of The 12th International Workshop on Semantic Evaluation
%D 2018
%8 jun
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F gee-wang-2018-psyml
%X In this paper, we describe the first attempt to perform transfer learning from sentiment to emotions. Our system employs Long Short-Term Memory (LSTM) networks, including bidirectional LSTM (biLSTM) and LSTM with attention mechanism. We perform transfer learning by first pre-training the LSTM networks on sentiment data before concatenating the penultimate layers of these networks into a single vector as input to new dense layers. For the E-c subtask, we utilize a novel approach to train models for correlated emotion classes. Our system performs 4/48, 3/39, 8/38, 4/37, 4/35 on all English subtasks EI-reg, EI-oc, V-reg, V-oc, E-c of SemEval 2018 Task 1: Affect in Tweets.
%R 10.18653/v1/S18-1056
%U https://aclanthology.org/S18-1056
%U https://doi.org/10.18653/v1/S18-1056
%P 369-376
Markdown (Informal)
[psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis](https://aclanthology.org/S18-1056) (Gee & Wang, SemEval 2018)
ACL