LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification
| Autor: | Maximilien Servajean, Waleed Ragheb, Sandra Bringay, Jérôme Azé |
|---|---|
| Přispěvatelé: | ADVanced Analytics for data SciencE (ADVANSE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Focus (computing)
Word embedding Computer science business.industry media_common.quotation_subject Emotion detection 02 engineering and technology computer.software_genre SemEval [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL] Task (project management) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Conversation [INFO.EIAH]Computer Science [cs]/Technology for Human Learning Artificial intelligence Transfer of learning business computer Natural language processing media_common |
| Zdroj: | 13th International Workshop on Semantic Evaluation in NAACL-HLT SemEval: Semantic Evaluation in NAACL-HLT SemEval: Semantic Evaluation in NAACL-HLT, Jun 2019, Minneapolis, MN, United States. pp.251-255 SemEval@NAACL-HLT |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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