论文标题
Convlab-3：基于统一数据格式的灵活对话系统工具包
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format
论文作者
论文摘要
以任务为导向的对话（TOD）系统作为数字助手的功能，指导用户执行各种任务，例如预订航班或寻找餐厅。现有用于构建TOD系统的工具包通常在交付具有用户友好体验的全面数据，模型和实验环境中差不多。我们介绍Convlab-3：一个多方面的对话系统工具包，该工具包制成，旨在弥合这一差距。我们的统一数据格式简化了各种数据集和模型的集成，从而大大降低了研究概括和转移的复杂性和成本。通过坚固的增强学习（RL）工具增强，具有简化的培训过程，深入的评估工具以及选择用户模拟器，Convlab-3支持了稳健对话策略的快速开发和评估。通过一项广泛的研究，我们证明了转移学习和RL的功效，并展示了Convlab-3不仅是经验丰富的研究人员的强大工具，而且还是新移民的可访问平台。
Task-oriented dialogue (TOD) systems function as digital assistants, guiding users through various tasks such as booking flights or finding restaurants. Existing toolkits for building TOD systems often fall short of in delivering comprehensive arrays of data, models, and experimental environments with a user-friendly experience. We introduce ConvLab-3: a multifaceted dialogue system toolkit crafted to bridge this gap. Our unified data format simplifies the integration of diverse datasets and models, significantly reducing complexity and cost for studying generalization and transfer. Enhanced with robust reinforcement learning (RL) tools, featuring a streamlined training process, in-depth evaluation tools, and a selection of user simulators, ConvLab-3 supports the rapid development and evaluation of robust dialogue policies. Through an extensive study, we demonstrate the efficacy of transfer learning and RL and showcase that ConvLab-3 is not only a powerful tool for seasoned researchers but also an accessible platform for newcomers.
