LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification

Waleed Ragheb 1 Jérôme Azé 1 Sandra Bringay 1 Maximilien Servajean 1
1 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
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. 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.
Complete list of metadatas

https://hal-lirmm.ccsd.cnrs.fr/lirmm-02145395
Contributor : Waleed Ragheb <>
Submitted on : Sunday, June 2, 2019 - 6:09:29 PM
Last modification on : Saturday, June 15, 2019 - 1:26:45 AM

Identifiers

  • HAL Id : lirmm-02145395, version 1

Collections

Citation

Waleed Ragheb, Jérôme Azé, Sandra Bringay, Maximilien Servajean. LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification. SemEval: Semantic Evaluation in NAACL-HLT, Jun 2019, Minneapolis, MN, United States. pp.251-255. ⟨lirmm-02145395⟩

Share

Metrics

Record views

18

Files downloads

8