On using the DIET architecture for sentiment analysis and emotion detection
Miguel Arevalillo‐Herráez, Pablo Arnau‐González, Inés Bravo-Cabrera, Naeem Ramzan · 2022
The Dual Intent and Entity Transformer (DIET) architecture has recently been proposed to perform intent classification and entity recognition in conversational agents. In this paper, we show that this architecture is also effective at other common tasks, such as sentiment analysis and emotion classification. The results have been validated in 4 different datasets and they show that DIET exhibits a comparative performance to other state-of-the-art methods, at the same time it provides a low code and fully configurable alternative that can be easily trained and deployed by using the Rasa conversational toolkit.