论文标题
板岩：从自由墨水中提取任务提取的序列标签方法
SLATE: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content
论文作者
论文摘要
我们提出了Slate，这是一种从虚拟白板上的自由形式内容（例如数字手写（或“墨水”）注释等自由形式内容中提取任务的序列标记方法。我们的方法使我们能够创建一个单一的低延迟模型，以同时将这些句子的句子细分和分类为任务/非任务句子。 Slate的表现极大地优于基线两模型（句子分割，然后是分类模型）方法，达到了84.4％的任务F1得分，句子分割（边界相似性）得分为88.4％，比基线降低了三倍。此外，我们还提供了解决在墨水域上执行NLP的挑战的见解。我们为这项新任务发布了代码和数据集。
We present SLATE, a sequence labeling approach for extracting tasks from free-form content such as digitally handwritten (or "inked") notes on a virtual whiteboard. Our approach allows us to create a single, low-latency model to simultaneously perform sentence segmentation and classification of these sentences into task/non-task sentences. SLATE greatly outperforms a baseline two-model (sentence segmentation followed by classification model) approach, achieving a task F1 score of 84.4%, a sentence segmentation (boundary similarity) score of 88.4% and three times lower latency compared to the baseline. Furthermore, we provide insights into tackling challenges of performing NLP on the inking domain. We release both our code and dataset for this novel task.
