#自然语言处理#💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
翻译 - 💬开源机器学习框架可自动执行基于文本和语音的对话：NLU，对话管理，连接到Slack，Facebook等-创建聊天机器人和语音助手
#自然语言处理#Rasa Core is now part of the Rasa repo: An open source machine learning framework to automate text-and voice-based conversations
翻译 - Rasa Core现在是Rasa回购的一部分：一个开源的机器学习框架，可自动进行基于文本和语音的对话
#自然语言处理#CakeChat: Emotional Generative Dialog System
#自然语言处理#This repo contains implementation of different architectures for emotion recognition in conversations.
Welcome to the Bot Framework Solutions repository which is the home for a set of templates and solutions to help build advanced conversational experiences using Azure Bot Service and Bot Framework. Mi...
翻译 - 欢迎使用Bot Framework解决方案存储库，该存储库提供了一组模板和解决方案，可帮助使用Azure Bot Service和Bot Framework建立高级的对话体验。 Microsoft Bot Framework是用于构建企业级对话式AI体验的综合框架。
Enterprise-grade open source GUI platform for Rasa teams
#自然语言处理#An open source Ruby framework for text and voice chatbots. 🤖
翻译 - 一个用于文本和语音聊天机器人的开源Ruby框架。 🤖
#自然语言处理#Dialogflow Web Integration. Supports rich components
#自然语言处理#Attention-based multimodal fusion for sentiment analysis
#自然语言处理#This repository contains a new generative model of chatbot based on seq2seq modeling.
#自然语言处理#RASA chatbot use case boilerplate
#自然语言处理#Agentic AI platform that harnesses Visual LLM Chaining to build proactive digital assistants
Kotlin framework for conversational voice assistants and chatbots development
#自然语言处理#IssuesにNLP（自然言語処理）に関連するの論文を読んだまとめを書いています．雑です．🚧 マークは編集中の論文です（事実上放置のものも多いです）．🍡 マークは概要のみ書いてます（早く見れる的な意味で団子）．
#自然语言处理#Explore LangChain and build powerful chatbots that interact with your own data. Gain insights into document loading, splitting, retrieval, question answering, and more.
#数据仓库#Overview of venues, research themes and datasets relevant for conversational search.
CORE is a plug-and-play conversational agent for any recommender system.
#自然语言处理#This repository contains PyTorch implementation for the baseline models from the paper Utterance-level Dialogue Understanding: An Empirical Study
Self-hosted AI voice agent
