COIN : Conversational Interactive Networks for Emotion Recognition in Conversation (2021)

Abstract: Emotion recognition in conversation has received considerable attention recently because of its practical industrial applications. Existing methods tend to overlook the immediate mutual interaction between different speakers in the speaker-utterance level, or apply single speaker-agnostic RNN for utterances from different speakers. We propose COIN, a conversational interactive model to mitigate this problem by applying state mutual interaction within history contexts. In addition, we introduce a stacked global interaction module to capture the contextual and inter-dependency representation in a hierarchical manner. To improve the robustness and generalization during training, we generate adversarial examples by applying the minor perturbations on multimodal feature inputs, unveiling the benefits of adversarial examples for emotion detection. The proposed model empirically achieves the current state-of-the-art results on the IEMOCAP benchmark dataset.

## introduction emotion recognition in conversation (erc) has attracted extensive interests due to the prevalence of user-generated contents on social media platforms, such as conversational messages and videos @xcite @xcite , which aims to detect the speaker's emotions and sentiments within the context of human conversations. recent works on erc adopted recurrent neural networks (rnns) to firstly learn the sequential utterances in conversations and then leveraged highlevel context extractor, such as cmn @xcite , dialoguernn @xcite , dialoguegcn @xcite , to capture the global contextual representation for emotion detection. ## methodology this section is orgnized as follows: sec. 2.1 describes the definition of erc task; sec. 2.2 introduces the approach to extracting multimodal features; sec. 2.3 gives a detailed description of the proposed model. ## conclusion we propose a new dialogue contextual interaction architecture to focus on the compact interaction for both speaker-level dialogue history and current utterance. by adopting adversarial training, our model achieves the sota performance on the iemocap dataset for emotion recognition in conversation. in the future, multimodal fusion methods could be investigated to capture richer modelilyinteractive representations at modality level.