论文标题
M2R2：缺失模式稳健的情绪识别框架，并具有迭代数据增强
M2R2: Missing-Modality Robust emotion Recognition framework with iterative data augmentation
论文作者
论文摘要
本文讨论了话语级别的方式缺少问题，而对话（ERC）任务中情绪识别的模式不确定。目前的模型通常通过其当前的话语和背景来预测说话者的情绪，这会大大缺失。我们的工作提出了一个框架缺失模式的稳健情绪识别（M2R2），该框架通过学识渊博的共同表示，通过迭代数据增强来训练情感识别模型。首先，一个称为党派关注网络（Panet）的网络旨在对情绪进行分类，该网络跟踪所有说话者的状态和环境。说话者与其他参与者与对话主题之间的注意机制用于分散对多次和多方话语的依赖，而不是可能的不完整。此外，定义了用于模态失误问题的通用表示学习（CRL）问题。这里使用对抗策略改进的数据插补方法来构建额外的功能以增强数据。广泛的实验和案例研究验证了我们方法对基准对模式失误的情绪识别的有效性。
This paper deals with the utterance-level modalities missing problem with uncertain patterns on emotion recognition in conversation (ERC) task. Present models generally predict the speaker's emotions by its current utterance and context, which is degraded by modality missing considerably. Our work proposes a framework Missing-Modality Robust emotion Recognition (M2R2), which trains emotion recognition model with iterative data augmentation by learned common representation. Firstly, a network called Party Attentive Network (PANet) is designed to classify emotions, which tracks all the speakers' states and context. Attention mechanism between speaker with other participants and dialogue topic is used to decentralize dependence on multi-time and multi-party utterances instead of the possible incomplete one. Moreover, the Common Representation Learning (CRL) problem is defined for modality-missing problem. Data imputation methods improved by the adversarial strategy are used here to construct extra features to augment data. Extensive experiments and case studies validate the effectiveness of our methods over baselines for modality-missing emotion recognition on two different datasets.
