论文标题
一种简单有效的多任务学习方法，用于有条件的对话生成
A Simple and Efficient Multi-Task Learning Approach for Conditioned Dialogue Generation
论文作者
论文摘要
条件对话的产生遭受了标记的响应的稀缺性。在这项工作中，我们利用标有与该条件有关的标记的非二元格文本数据，这些数据更容易收集。我们提出了一种多任务学习方法，以利用标记的对话和文本数据。这3个任务共同优化了相同的预训练的变压器 - 标记为对话数据的条件对话生成任务，条件语言编码任务以及标记为文本数据的条件语言生成任务。实验结果表明，我们的方法通过利用标记的文本来优于最新模型，并且与以前的方法相比，它在性能方面也获得了更大的改进，以利用文本数据。
Conditioned dialogue generation suffers from the scarcity of labeled responses. In this work, we exploit labeled non-dialogue text data related to the condition, which are much easier to collect. We propose a multi-task learning approach to leverage both labeled dialogue and text data. The 3 tasks jointly optimize the same pre-trained Transformer -- conditioned dialogue generation task on the labeled dialogue data, conditioned language encoding task and conditioned language generation task on the labeled text data. Experimental results show that our approach outperforms the state-of-the-art models by leveraging the labeled texts, and it also obtains larger improvement in performance comparing to the previous methods to leverage text data.
