EDA: ニューラルアノテータのアンサンブルによる感情対話行為の拡充
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators
対話行為コーパスで学習した複数の再帰型ニューラルモデルのアンサンブルで感情コーパス(IEMOCAPとMELD)に対話行為ラベルを付与し、感情と対話行為の共起関係を分析してEDAコーパスを公開した論文。
著者: Chandrakant Bothe, Cornelius Weber, Sven Magg, Stefan Wermter
分類: cs.CL, cs.AI, cs.RO
原文アブストラクト
The recognition of emotion and dialogue acts enriches conversational analysis and help to build natural dialogue systems. Emotion interpretation makes us understand feelings and dialogue acts reflect the intentions and performative functions in the utterances. However, most of the textual and multi-modal conversational emotion corpora contain only emotion labels but not dialogue acts. To address this problem, we propose to use a pool of various recurrent neural models trained on a dialogue act corpus, with and without context. These neural models annotate the emotion corpora with dialogue act labels, and an ensemble annotator extracts the final dialogue act label. We annotated two accessible multi-modal emotion corpora: IEMOCAP and MELD. We analyzed the co-occurrence of emotion and dialogue act labels and discovered specific relations. For example, Accept/Agree dialogue acts often occur with the Joy emotion, Apology with Sadness, and Thanking with Joy. We make the Emotional Dialogue Acts (EDA) corpus publicly available to the research community for further study and analysis.
