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Found 5 papers, 1 papers with code
Contextual Augmentation of Pretrained Language Models for Emotion Recognition in Conversations
We firstly use ELECTRA which is a state-of-the-art pretrained language model and validate the performance on emotion recognition in conversations.
Modeling Inter-Speaker Relationship in XLNet for Contextual Spoken Language Understanding
We propose two methods to capture relevant history information in a multi-turn dialogue by modeling inter-speaker relationship for spoken language understanding (SLU).
Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding
To capture salient contextual information for spoken language understanding (SLU) of a dialogue, we propose time-aware models that automatically learn the latent time-decay function of the history without a manual time-decay function.
Self-Attention-Based Message-Relevant Response Generation for Neural Conversation Model
Using a sequence-to-sequence framework, many neural conversation models for chit-chat succeed in naturalness of the response.
Multiple Range-Restricted Bidirectional Gated Recurrent Units with Attention for Relation Classification
Most of neural approaches to relation classification have focused on finding short patterns that represent the semantic relation using Convolutional Neural Networks (CNNs) and those approaches have generally achieved better performances than using Recurrent Neural Networks (RNNs).
