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Found 13 papers, 3 papers with code
SEPRG: Sentiment aware Emotion controlled Personalized Response Generation
We design a Transformer based Dialogue Generation framework, that generates responses that are sensitive to the emotion of the user and corresponds to the persona and sentiment as well.
Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems
Even the most well-intended and reasoned persuasive conversations can fall through in the absence of empathetic connection between the speaker and listener.
PoliSe: Reinforcing Politeness Using User Sentiment for Customer Care Response Generation
The interaction between a consumer and the customer service representative greatly contributes to the overall customer experience.
A Unified Framework for Slot based Response Generation in a Multimodal Dialogue System
Lastly, a decoder generates the corresponding response for the given dialogue context and the extracted slot values.
Prompt-Based Editing for Text Style Transfer
In this paper, we present a prompt-based editing approach for text style transfer.
A Unified Framework for Emotion Identification and Generation in Dialogues
Social chatbots have gained immense popularity, and their appeal lies not just in their capacity to respond to the diverse requests from users, but also in the ability to develop an emotional connection with users.
EmoInHindi: A Multi-label Emotion and Intensity Annotated Dataset in Hindi for Emotion Recognition in Dialogues
The long-standing goal of Artificial Intelligence (AI) has been to create human-like conversational systems.
Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic
In this work, we address the explainability of NLI by weakly supervised logical reasoning, and propose an Explainable Phrasal Reasoning (EPR) approach.
MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations
Multi-label emotion detection in conversations is a significant task that provides the ability to the system to understand the various emotions of the users interacting.
MultiDM-GCN: Aspect-guided Response Generation in Multi-domain Multi-modal Dialogue System using Graph Convolutional Network
In this work, we present a multi-modal conversational framework for a task-oriented dialogue setup that generates the responses following the different aspects of a product or service to cater to the user{'}s needs.
Incorporating Politeness across Languages in Customer Care Responses: Towards building a Multi-lingual Empathetic Dialogue Agent
Our system is competent in generating responses in different languages (here, English and Hindi) depending on the customer{'}s preference and also is able to converse with humans in an empathetic manner to ensure customer satisfaction and retention.
Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System
Multimodal dialogue systems have opened new frontiers in the traditional goal-oriented dialogue systems.
Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network
We use real interactions on Twitter between customer care professionals and aggrieved customers to create a large conversational dataset having both forms of agent responses: {`}generic{'} and {`}courteous{'}.
