论文标题
aargh！面向任务的对话框的端到端检索产生
AARGH! End-to-end Retrieval-Generation for Task-Oriented Dialog
论文作者
论文摘要
我们介绍了AARGH，这是一个面向任务的对话框系统，将单个模型中的检索和生成方法结合在一起，旨在改善对话框管理和输出的词汇多样性。该模型采用了一种新的响应选择方法，基于动作感知训练目标和简化的单名检索架构，该方法使我们能够构建一个端到端检索增强生成模型，其中检索和生成共享大多数参数。在Multiwoz数据集上，我们表明我们的方法与最先进的基线相比，在维持或改善状态跟踪和上下文响应生成性能的同时，产生了更多的输出。
We introduce AARGH, an end-to-end task-oriented dialog system combining retrieval and generative approaches in a single model, aiming at improving dialog management and lexical diversity of outputs. The model features a new response selection method based on an action-aware training objective and a simplified single-encoder retrieval architecture which allow us to build an end-to-end retrieval-enhanced generation model where retrieval and generation share most of the parameters. On the MultiWOZ dataset, we show that our approach produces more diverse outputs while maintaining or improving state tracking and context-to-response generation performance, compared to state-of-the-art baselines.
