论文标题
朝向端到端的开放对话机阅读
Towards End-to-End Open Conversational Machine Reading
论文作者
论文摘要
在开放式回归对话机阅读（OR-CMR）任务中，需要计算机来对对话历史记录和文本知识库进行多转弯问题。现有的作品通常利用两个独立的模块来解决此问题的连续两个子任务：首先是硬标签决策，其次是通过各种需要推理方法来帮助生成的。这种通常的级联建模容易受到错误传播的影响，并防止两个子任务始终如一地进行优化。在这项工作中，我们以完全端到端的样式建模为统一的文本到文本任务。 Sharc和OR-Sharc数据集的实验显示了我们提出的端到端框架对两个子任务的有效性，从而实现了新的最新结果。进一步的消融研究支持我们的框架可以推广到不同的骨干模型。
In open-retrieval conversational machine reading (OR-CMR) task, machines are required to do multi-turn question answering given dialogue history and a textual knowledge base. Existing works generally utilize two independent modules to approach this problem's two successive sub-tasks: first with a hard-label decision making and second with a question generation aided by various entailment reasoning methods. Such usual cascaded modeling is vulnerable to error propagation and prevents the two sub-tasks from being consistently optimized. In this work, we instead model OR-CMR as a unified text-to-text task in a fully end-to-end style. Experiments on the ShARC and OR-ShARC dataset show the effectiveness of our proposed end-to-end framework on both sub-tasks by a large margin, achieving new state-of-the-art results. Further ablation studies support that our framework can generalize to different backbone models.
