ConvLab
ConvLab is an open-source toolkit for building conversational AI agents. In just a few lines of code, it enables rapid prototyping of multi-modal, multi-agent conversation systems across different conversation scenarios.
opensource
toolkit
conversational-agents
rapid-prototyping
multimodal
multiagent
ConvLab: Open-Source Conversational AI Toolkit
ConvLab is an open-source toolkit for building conversational AI agents. In just a few lines of code, it enables rapid prototyping of multi-modal, multi-agent conversation systems across different conversation scenarios.
What is ConvLab?
ConvLab is an open-source toolkit for building conversational AI agents. It provides a set of reusable components that can be easily customized and assembled to create multi-modal, multi-agent conversational systems for various conversation scenarios such as chit-chat, recommendation, negotiation and task-completion.
Some key features of ConvLab include:
- Modular architecture - Components such as dialog state tracking, policy learning, generation and comprehension can be easily swappable
- Multi-modal support - Supports both text and speech as input and output modalities
- Reproducible experiments - Provides a standard dataset and evaluation metrics for reproducible research
- High customizability - Components have well defined APIs that allow easy customization and extension
- Multiple reference models - Comes with several reference models that can be used for pre-training or adapted for your own agents
With ConvLab's flexible architecture and breadth of included capabilities and models, developers can focus on innovating at the architecture-level while reusing state-of-art libraries for individual components. This enables rapid prototyping and reduces the system building effort significantly.
ConvLab Features
Features
- Multi-modal multi-agent conversation modeling
- Pre-built modules for NLU, DST, Policy and NLG
- Reproducible experiment configuration
- Evaluation with user simulators and human evaluations
Pricing
- Open Source
Pros
Modular and extensible architecture
Pre-built reference models
Active community and development
Cons
Limited out-of-the-box support for commercial applications
Steep learning curve for non-ML experts
Official Links
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