论文标题
掩盖编排：多角色对话表示学习的多任务预处理学习
Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning
论文作者
论文摘要
多角色对话的理解包括各种各样的任务，例如问答，ACT分类，对话摘要等。虽然对话COLIDOA虽然可用，但对于特定的学习任务而言，标有数据的数据可能是高度稀缺和昂贵的。在这项工作中，我们使用各种类型的对话上下文表示学习，无监督的预处理任务，在这些任务中，根据话语的性质和多角色对话的结构，自然地给出了训练目标。同时，为了找到对话摘要/提取的基本信息，预处理过程可以实现外部知识集成。通过三个不同的对话数据集以及许多下游对话挖掘任务对所提出的微调预处理机制进行了全面评估。结果表明，所提出的预处理机制显着有助于所有下游任务，而无需歧视不同的编码器。
Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classification, dialogue summarization etc. While dialogue corpora are abundantly available, labeled data, for specific learning tasks, can be highly scarce and expensive. In this work, we investigate dialogue context representation learning with various types unsupervised pretraining tasks where the training objectives are given naturally according to the nature of the utterance and the structure of the multi-role conversation. Meanwhile, in order to locate essential information for dialogue summarization/extraction, the pretraining process enables external knowledge integration. The proposed fine-tuned pretraining mechanism is comprehensively evaluated via three different dialogue datasets along with a number of downstream dialogue-mining tasks. Result shows that the proposed pretraining mechanism significantly contributes to all the downstream tasks without discrimination to different encoders.
