# Conversational Transfer Learning for Emotion Recognition

arXiv: https://arxiv.org/abs/1910.04980 · HTML: https://mlsanity.com/paper/1910.04980/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition](https://mlsanity.com/paper/2211.11256/) (2022)
- [Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations](https://mlsanity.com/paper/2407.00119/) (2024)
- [Discovering Emotion and Reasoning its Flip in Multi-Party Conversations using Masked Memory Network and Transformer](https://mlsanity.com/paper/2103.12360/) (2021)
- [UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause](https://mlsanity.com/paper/2404.00403/) (2024)
- [MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations](https://mlsanity.com/paper/2102.01263/) (2021)
- [Emotion Dynamics Modeling via BERT](https://mlsanity.com/paper/2104.07252/) (2021)
- [Cross Domain Emotion Recognition using Few Shot Knowledge Transfer](https://mlsanity.com/paper/2110.05021/) (2021)
- [FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition](https://mlsanity.com/paper/2307.12221/) (2023)

## Abstract (arXiv)

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.