LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2019

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

Waleed Ragheb
Jérôme Azé
Sandra Bringay

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.
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Dates and versions

lirmm-02145395 , version 1 (02-06-2019)

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  • HAL Id : lirmm-02145395 , version 1

Cite

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⟩
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