Conversational Transfer Learning for Emotion Recognition

TL;DR

This paper introduces TL-ERC, a transfer learning approach that leverages generative conversational models to improve emotion recognition in conversations, especially when annotated data is limited.

Contribution

The paper proposes a novel transfer learning method using hierarchical dialogue models to enhance emotion recognition in conversations, incorporating inter-sentential context modeling.

Findings

TL-ERC improves emotion recognition accuracy across multiple datasets.

The approach shows increased robustness with limited training data.

TL-ERC converges faster than traditional supervised models.

Abstract

Recognizing emotions in conversations is a challenging task due to the presence of contextual dependencies governed by self- and inter-personal influences. Recent approaches have focused on modeling these dependencies primarily via supervised learning. However, purely supervised strategies demand large amounts of annotated data, which is lacking in most of the available corpora in this task. To tackle this challenge, we look at transfer learning approaches as a viable alternative. Given the large amount of available conversational data, we investigate whether generative conversational models can be leveraged to transfer affective knowledge for detecting emotions in context. We propose an approach, TL-ERC, where we pre-train a hierarchical dialogue model on multi-turn conversations (source) and then transfer its parameters to a conversational emotion classifier (target). In addition to the popular practice of using pre-trained sentence encoders, our approach also incorporates recurrent parameters that model inter-sentential context across the whole conversation. Based on this idea, we perform several experiments across multiple datasets and find improvement in performance and robustness against limited training data. TL-ERC also achieves better validation performances in significantly fewer epochs. Overall, we infer that knowledge acquired from dialogue generators can indeed help recognize emotions in conversations.