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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/S19-2042.pdf
Data
EmoContext