arXiv (All)
open access
EmoLASP: Emotion Recognition with Language Models and Answer Set Programming
Emotion recognition in conversations is increasingly tackled with language models, but these models can be unstable and expensive to fine-tune or to prompt with long dialogue histories. We propose EmoLASP, a framework that combines a language model with declarative reasoning via Answer Set Programmi
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- EmoLASP: Emotion Recognition with Language Models and Answer Set Programming#789255
- Computer says "No": The Case Against Empathetic Conversational AI#17704
- OneEmo: A Unified Multimodal Reasoning Model for Emotion Perception, Understanding, and Interaction#974520
- A System for Automatic Emotion Attribution based on a Commonsense Reasoning Framework#748452
- Modeling word-level emotions in context to enhance sentence-level emotion detection / Akram Sadat Hosseini ; Betreuer: Steffen Staab#1119762
Record · ID 793259
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive