Conversational Transfer Learning for Emotion Recognition

11 Oct 2019  ·  Devamanyu Hazarika, Soujanya Poria, Roger Zimmermann, Rada Mihalcea ·

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.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Emotion Recognition in Conversation DailyDialog VHRED Micro-F1 48.4 # 20
Emotion Recognition in Conversation IEMOCAP VHRED Weighted-F1 59.56 # 55

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