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什么是对话学习器?What is Conversation Learner?

对话学习器使你能够构建和教授从示例交互中学习的对话接口。Conversation Learner enables you to build and teach conversational interfaces that learn from example interactions.

与传统方法不同,对话学习器会考虑对话的端到端上下文来改进响应并提供更具吸引力的用户体验。Unlike traditional approaches, Conversation Learner considers the end-to-end context of a dialogue to improve responses and deliver more compelling user experiences. Conversation Learner 涉及一组广泛的面向任务的用例,在后台应用机器学习,使机器人和智能代理不太可能阻挠用户、增加客户服务成本,并促进更直观的交互。Spanning a broad set of task-oriented use cases, Conversation Learner applies machine learning behind the scenes to make bots and intelligent agents less likely to frustrate users, incur additional customer service costs, and spur more intuitive interactions.

首先,开发人员输入他们想要模拟的原型对话。Developers start by entering prototypical dialogs they want to imitate. 当输入更多对话时,模型会进行学习。The Model learns as more dialogs are entered. 模型运行良好后,就可以将机器人部署到最终用户。Once the Model is working well, the Bot can be deployed to end users. 对话学习器记录与用户的对话,开发人员可以查看这些对话。Conversation Learner logs conversations with users, and the developer can review them. 如果发现错误,开发人员可以进行现场更正,并对模型进行重新训练,模型立即可供使用。If mistakes are spotted, the developer can make an on-the-spot correction, and the model is retrained and available for use immediately.

这种方法会减少对话控制逻辑的手动编码,并使企业所有者或领域专家能够在没有先前机器学习知识的情况下促成对话接口。This approach reduces manual coding of dialogue control logic and enables business owners or domain experts to contribute to a conversational interface without prior machine learning knowledge. 无论是作为机器人、智能设备还是智能代理的一部分进行部署,对话学习器都可以快速迭代新技能、行为或能力,并快速提高其质量。Whether it’s deployed as part of a bot, smart device, or intelligent agent, Conversation Learner can rapidly iterate new skills, behaviors, or competencies and quickly improve their quality.

对话学习器使开发人员能够加快产品上市速度,并通过 Microsoft Bot Framework 或仅仅使用其自己的基础结构跨多个对话渠道推动成功对话。Conversation Learner empowers developers to increase speed-to-market and drive successful dialogues across multiple conversational channels through the Microsoft Bot Framework, or standalone using their own infrastructure.

摘要和要点:Summary and highlights:

  • 对话学习器是构建面向任务型机器人的一种 AI 优先方式。Conversation Learner is an AI-first way of building task-oriented bots.

  • 它依赖于端到端循环神经网络 (LSTM),并直接从多轮次的对话示例中学习。It relies on an end-to-end recurrent neural network (LSTM), and learns directly from multi-turn examples of conversations.

  • 使设计人员、开发人员、业务用户和呼叫中心工作人员能够构建和维护机器人。Enables designers, developers, business users, and call center workers to build and maintain bots.

  • 提供在代码中表达业务规则和常识的能力。Provides the ability to express business rules and common sense in code.

  • 在教学过程中,神经网络模型用于在对话中对下一组预期操作进行评分。During teaching sessions, the neural network model is used to score the next set of expected actions in the conversation. 然后,机器人开发人员可以选择正确的操作,并训练网络以提供正确的响应。The bot developer can then select the correct action, and train the network to provide the proper response.

  • 训练完成后,开发人员可以使用用户交互中的记录对话来修正机器人响应并重新训练模型。After training is complete, the developer can use the log dialogs from the user interactions to make corrections to bot responses and retrain the model.

  • 可以调用领域特定的 API 和第三方 API 来完成任务。Can call domain-specific and third-party APIs to complete tasks.