论文标题
实体跟踪通过有效使用多任务学习模型并提及指导解码
Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decoding
论文作者
论文摘要
通过多任务学习的交叉任务知识转移最近在一般NLP任务中取得了显着进步。但是，由于其独特的表述，即在遵循结构约束时跟踪事件流程，因此对程序文本的实体跟踪并未从这种知识转移中受益。最先进的实体跟踪方法要么设计复杂的模型架构，要么依靠特定于任务的预训练来取得良好的结果。为此，我们提出了Meet，这是一种多任务学习的实体跟踪方法，该方法利用从一般领域任务中获得的知识来改善实体跟踪。具体而言，符合First Fielt-FineTunes T5，这是一种预先训练的多任务学习模型，具有实体跟踪特殊的QA格式，然后采用我们的自定义解码策略来满足结构约束。在两个流行的实体跟踪数据集上满足成就的最新性能，即使它不需要任何特定于任务的架构设计或预培训。
Cross-task knowledge transfer via multi-task learning has recently made remarkable progress in general NLP tasks. However, entity tracking on the procedural text has not benefited from such knowledge transfer because of its distinct formulation, i.e., tracking the event flow while following structural constraints. State-of-the-art entity tracking approaches either design complicated model architectures or rely on task-specific pre-training to achieve good results. To this end, we propose MeeT, a Multi-task learning-enabled entity Tracking approach, which utilizes knowledge gained from general domain tasks to improve entity tracking. Specifically, MeeT first fine-tunes T5, a pre-trained multi-task learning model, with entity tracking-specialized QA formats, and then employs our customized decoding strategy to satisfy the structural constraints. MeeT achieves state-of-the-art performances on two popular entity tracking datasets, even though it does not require any task-specific architecture design or pre-training.
