A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis
Jean-Benoit Delbrouck, Noé Tits, Mathilde Brousmiche, Stéphane Dupont
Abstract
Understanding expressed sentiment and emotions are two crucial factors in human multimodal language. This paper describes a Transformer-based joint-encoding (TBJE) for the task of Emotion Recognition and Sentiment Analysis. In addition to use the Transformer architecture, our approach relies on a modular co-attention and a glimpse layer to jointly encode one or more modalities. The proposed solution has also been submitted to the ACL20: Second Grand-Challenge on Multimodal Language to be evaluated on the CMU-MOSEI dataset. The code to replicate the presented experiments is open-source .- Anthology ID:
- 2020.challengehml-1.1
- Volume:
- Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)
- Month:
- July
- Year:
- 2020
- Address:
- Seattle, USA
- Editors:
- Amir Zadeh, Louis-Philippe Morency, Paul Pu Liang, Soujanya Poria
- Venue:
- Challenge-HML
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1–7
- Language:
- URL:
- https://aclanthology.org/2020.challengehml-1.1
- DOI:
- 10.18653/v1/2020.challengehml-1.1
- Cite (ACL):
- Jean-Benoit Delbrouck, Noé Tits, Mathilde Brousmiche, and Stéphane Dupont. 2020. A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis. In Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML), pages 1–7, Seattle, USA. Association for Computational Linguistics.
- Cite (Informal):
- A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis (Delbrouck et al., Challenge-HML 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.challengehml-1.1.pdf
- Data
- CMU-MOSEI