LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification
Waleed Ragheb, Jérôme Azé, Sandra Bringay, Maximilien Servajean
Abstract
This paper addresses the problem of modeling textual conversations and detecting emotions. Our proposed model makes use of 1) deep transfer learning rather than the classical shallow methods of word embedding; 2) self-attention mechanisms to focus on the most important parts of the texts and 3) turn-based conversational modeling for classifying the emotions. The approach does not rely on any hand-crafted features or lexicons. Our model was evaluated on the data provided by the SemEval-2019 shared task on contextual emotion detection in text. The model shows very competitive results.- Anthology ID:
- S19-2042
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Editors:
- Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 251–255
- Language:
- URL:
- https://aclanthology.org/S19-2042
- DOI:
- 10.18653/v1/S19-2042
- Cite (ACL):
- Waleed Ragheb, Jérôme Azé, Sandra Bringay, and Maximilien Servajean. 2019. LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 251–255, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification (Ragheb et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/S19-2042.pdf
- Data
- EmoContext