Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on previously learnt services. Motivated by the insight that dialogue state tracking (DST), a crucial component of dialogue systems that estimates the user's goal as a conversation proceeds, is a simple natural language understanding task, we propose reformulating it as a bundle of granular example-guided question answering tasks to minimize the task shift between services and thus benefit continual learning. Our approach alleviates service-specific memorization and teaches a model to contextualize the given question and example to extract the necessary information from the conversation. We find that a model with just 60M parameters can achieve a significant boost by learning to learn from in-context examples retrieved by a retriever trained to identify turns with similar dialogue state changes. Combining our method with dialogue-level memory replay, our approach attains state of the art performance on DST continual learning metrics without relying on any complex regularization or parameter expansion methods.

翻译：对话系统需要频繁更新以适配新服务，但直接使用新服务数据持续训练会导致先前学习服务性能衰减。基于对话状态追踪（对话系统在对话进程中估算用户目标的关键组件）本质上是简单的自然语言理解任务这一认识，我们提出将其重构为一组细粒度的示例引导问答任务，以最小化服务间的任务偏移并促进持续学习。该方法缓解了服务特定记忆问题，训练模型在给定问题和示例的上下文中提取对话必要信息。我们发现仅需6000万参数的模型，通过从经训练的检索器（用于识别具有相似对话状态变化的轮次）检索到的上下文示例中学习，即可获得显著性能提升。将本方法与对话级记忆重放相结合，在不依赖任何复杂正则化或参数扩展方法的情况下，在对话状态追踪持续学习指标上达到了当前最优性能。