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.

SLATE 使用序列标注方法从自由文本中提取任务，可用于数字手写笔记。该方法可以同时执行句子分割和分类，并在任务 / 非任务句子之间进行划分。该方法相较于基线模型 (outperforms a baseline)，任务 F1 得分为 84.4％，句子分割得分（边界相似性）为 88.4％。此外，该研究提供了应对数字手写领域中进行自然语言处理的挑战的见解，并提供了代码和数据集。

SLATE：一种从自由书写内容中提取任务的序列标注方法

SLATE: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content

Although DALL-E has shown an impressive ability of composition-based
systematic generalization in image generation, it requires the dataset of
text-image pairs and the compositionality is provided by the text. In contrast,
object-centric representation models like the Slot Attention model learn
composable representations without the text prompt. However, unlike DALL-E its
ability to systematically generalize for zero-shot generation is significantly
limited. In this paper, we propose a simple but novel slot-based autoencoding
architecture, called SLATE, for combining the best of both worlds: learning
object-centric representations that allows systematic generalization in
zero-shot image generation without text. As such, this model can also be seen
as an illiterate DALL-E model. Unlike the pixel-mixture decoders of existing
object-centric representation models, we propose to use the Image GPT decoder
conditioned on the slots for capturing complex interactions among the slots and
pixels. In experiments, we show that this simple and easy-to-implement
architecture not requiring a text prompt achieves significant improvement in
in-distribution and out-of-distribution (zero-shot) image generation and
qualitatively comparable or better slot-attention structure than the models
based on mixture decoders.

本文介绍了一种结合了 DALL-E 的构成性和 Slot Attention 模型的目标 - 中心表示模型的自动编码架构，名为 SLATE，可以在没有文本的情况下进行系统化的泛化，该模型基于 Image GPT 解码器，可以用于处理图像，实验结果表明，相较于混合解码器模型，该模型在内态和外态图像生成方面有着显著提高，并且具有与混合解码器模型相当或更好的感知机制结构。