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.- Anthology ID:
- S18-1056
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 369–376
- Language:
- URL:
- https://aclanthology.org/S18-1056
- DOI:
- 10.18653/v1/S18-1056
- Cite (ACL):
- Grace Gee and Eugene Wang. 2018. psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 369–376, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis (Gee & Wang, SemEval 2018)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/S18-1056.pdf