We therefore propose a hierarchical Gated Recurrent Unit (HiGRU) framework with a lower-level GRU to model the word-level inputs and an upper-level GRU to ...

Apr 9, 2019 · In this paper, we address three challenges in utterance-level emotion recognition in dialogue systems: (1) the same word can deliver different ...

This is the Pytorch implementation of HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition in NAACL-2019.

This paper proposes a hierarchical Gated Recurrent Unit (HiGRU) framework with a lower-level GRU to model the word-level inputs and an upper-levelGRU to ...

In this paper, we address three challenges in utterance-level emotion recognition in dialogue systems: (1) the same word can deliver different emotions in ...

In this paper, we address three challenges in utterance-level emotion recognition in dialogue systems: (1) the same word can deliver different emotions in ...

Lyu. HiGRU: Hierarchical Gated Re- current Units for Utterance-level Emotion Recognition. In. NAACL, pages 397–406, 2019.

Emotion recognition in conversation aims to identify the emotion of each consistent utterance in a conversation from several pre-defined emotions.

In this paper, we address some challenges in ULER in dialog systems. (1) The same utterance can deliver different emotions when it is in different contexts.

A HiGRU framework to better learn both the individual utterance embeddings and the contextual information of utterances. • Two progressive variants:.