@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",
    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/S18-1056/",
    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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        <namePart type="given">Grace</namePart>
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    <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.</abstract>
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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
%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 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