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Dialog policy optimization for task-oriented conversational agents in low resource setting using Reinforcement learning. The methodology is based on a novel probability based Self-play technique and a novel Reward based sampling technique that prioritizes failed dialogues over successful ones
Goal-oriented open-ended counselling chatbot. Modular goals are added according to dialogue history and executed by priority via conditional prompts passed to an LLM. Uses Vicuna 13B as base model.
Code tor the SIGDIAL 2019 paper Flexibly-Structured Model for Task-Oriented Dialogues. It implements a deep learning end-to-end differentiable dialogue system model
An empathetic counselling chatbot. Retrieval-based, uses finetuned LMs for emotion identification and to boost empathy, novelty and fluency of the retrieved responses. Backend: Python, frontend: Javascript.
NNDial is an open source toolkit for building end-to-end trainable task-oriented dialogue models. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.