论文标题
BORT：端到端以任务为导向的对话框的背部和Deno重建
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog
论文作者
论文摘要
典型的端到端端到端的对话框系统将上下文传输到对话框状态，并在此上产生响应，通常面临着从先前生成的不准确的对话框状态和响应的错误传播问题，尤其是在低资源场景中。为了减轻这些问题，我们提出了Bort，是针对端到端任务的对话框系统的背部和DeNo重建方法。为了提高对话框状态的准确性，返回重建用于从生成的对话框状态重建原始输入上下文，因为不准确的对话框状态无法恢复相应的输入上下文。为了增强模型减少误差传播影响的能力，使用重建重建来重建损坏的对话框状态和响应。在Multiwoz 2.0和Camrest676上进行的广泛实验显示了Bort的有效性。此外，Bort在零射门域和低资源场景中展示了其高级功能。
A typical end-to-end task-oriented dialog system transfers context into dialog state, and upon which generates a response, which usually faces the problem of error propagation from both previously generated inaccurate dialog states and responses, especially in low-resource scenarios. To alleviate these issues, we propose BORT, a back and denoising reconstruction approach for end-to-end task-oriented dialog system. Squarely, to improve the accuracy of dialog states, back reconstruction is used to reconstruct the original input context from the generated dialog states since inaccurate dialog states cannot recover the corresponding input context. To enhance the denoising capability of the model to reduce the impact of error propagation, denoising reconstruction is used to reconstruct the corrupted dialog state and response. Extensive experiments conducted on MultiWOZ 2.0 and CamRest676 show the effectiveness of BORT. Furthermore, BORT demonstrates its advanced capabilities in the zero-shot domain and low-resource scenarios.
