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Found 7 papers, 2 papers with code
ConvGenVisMo: Evaluation of Conversational Generative Vision Models
Conversational generative vision models (CGVMs) like Visual ChatGPT (Wu et al., 2023) have recently emerged from the synthesis of computer vision and natural language processing techniques.
Generating Emotionally Aligned Responses in Dialogues using Affect Control Theory
State-of-the-art neural dialogue systems excel at syntactic and semantic modelling of language, but often have a hard time establishing emotional alignment with the human interactant during a conversation.
Progressive Memory Banks for Incremental Domain Adaptation
The memory bank provides a natural way of IDA: when adapting our model to a new domain, we progressively add new slots to the memory bank, which increases the number of parameters, and thus the model capacity.
Affective Neural Response Generation
Existing neural conversational models process natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content.
Deep Active Learning for Dialogue Generation
We propose an online, end-to-end, neural generative conversational model for open-domain dialogue.
Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey
Automatic extraction of cause-effect relationships from natural language texts is a challenging open problem in Artificial Intelligence.
Yelp Dataset Challenge: Review Rating Prediction
Review websites, such as TripAdvisor and Yelp, allow users to post online reviews for various businesses, products and services, and have been recently shown to have a significant influence on consumer shopping behaviour.
