# Conversational Transfer Learning for Emotion Recognition Source: [Conversational Transfer Learning for Emotion Recognition]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:11:33.658Z Authors: Devamanyu Hazarika, Soujanya Poria, Roger Zimmermann, Rada Mihalcea Published: 2019-10-11T05:39:08.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Emotion Recognition in Conversation on SEMAINE](): Emotion Recognition in Conversation; SEMAINE; DV (r): 0.66. - [Emotion Recognition in Conversation on IEMOCAP](): Emotion Recognition in Conversation; IEMOCAP; F1 Score: 58.8. - [Emotion Recognition in Conversation on IEMOCAP 1.0 (test)](): Emotion Recognition in Conversation; IEMOCAP 1.0 (test); Weighted F1 Score: 58.6. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()