Translating training data into target languages has proven beneficial for
cross-lingual transfer. However, for structure extraction tasks, translating
data requires a label projection step, which translates input text and obtains
translated labels in the translated text jointly. Previous research in label
projection mostly compromises translation quality by either facilitating easy
identification of translated labels from translated text or using word-level
alignment between translation pairs to assemble translated phrase-level labels
from the aligned words. In this paper, we introduce CLAP, which first
translates text to the target language and performs contextual translation on
the labels using the translated text as the context, ensuring better accuracy
for the translated labels. We leverage instruction-tuned language models with
multilingual capabilities as our contextual translator, imposing the constraint
of the presence of translated labels in the translated text via instructions.
We compare CLAP with other label projection techniques for creating
pseudo-training data in target languages on event argument extraction, a
representative structure extraction task. Results show that CLAP improves by
2-2.5 F1-score over other methods on the Chinese and Arabic ACE05 datasets.

通过使用 CLAP 方法，首先将文本翻译成目标语言，并使用翻译后的文本作为上下文对标签进行语境翻译，从而提高了翻译标签的准确性。在事件论证提取等结构提取任务上，CLAP 相较于其他方法在中文和阿拉伯语 ACE05 数据集上提高了 2-2.5 个 F1 分数。

跨语言结构提取的上下文标签投影

Contextual Label Projection for Cross-Lingual Structure Extraction

Extracting structure information from dialogue data can help us better
understand user and system behaviors. In task-oriented dialogues, dialogue
structure has often been considered as transition graphs among dialogue states.
However, annotating dialogue states manually is expensive and time-consuming.
In this paper, we propose a simple yet effective approach for structure
extraction in task-oriented dialogues. We first detect and cluster possible
slot tokens with a pre-trained model to approximate dialogue ontology for a
target domain. Then we track the status of each identified token group and
derive a state transition structure. Empirical results show that our approach
outperforms unsupervised baseline models by far in dialogue structure
extraction. In addition, we show that data augmentation based on extracted
structures enriches the surface formats of training data and can achieve a
significant performance boost in dialogue response generation.

本文提出一种在面向任务的对话中提取结构化信息的方法，通过使用预训练模型检测和聚类可能的槽标记来逼近目标领域的对话本体，在此基础上推导出状态转移结构，实验结果表明这种方法在对话结构提取方面表现比无监督基线模型更好，此外，通过基于提取的结构进行数据增强可以大幅提高对话响应生成的性能。

基于任务的对话中的结构提取：基于槽聚类的方法

Structure Extraction in Task-Oriented Dialogues with Slot Clustering

Structure extraction from document images has been a long-standing research
topic due to its high impact on a wide range of practical applications. In this
paper, we share our findings on employing a hierarchical semantic segmentation
network for this task of structure extraction. We propose a prior based deep
hierarchical CNN network architecture that enables document structure
extraction using very high resolution(1800 x 1000) images. We divide the
document image into overlapping horizontal strips such that the network
segments a strip and uses its prediction mask as prior for predicting the
segmentation of the subsequent strip. We perform experiments establishing the
effectiveness of our strip based network architecture through ablation methods
and comparison with low-resolution variations. Further, to demonstrate our
network's capabilities, we train it on only one type of documents (Forms) and
achieve state-of-the-art results over other general document datasets. We
introduce our new human-annotated forms dataset and show that our method
significantly outperforms different segmentation baselines on this dataset in
extracting hierarchical structures. Our method is currently being used in
Adobe's AEM Forms for automated conversion of paper and PDF forms to modern
HTML based forms.

本研究基于层次语义分割网络，提出了一种基于先验模型的深层次 CNN 网络结构，用于高分辨率文档图像的结构提取，通过在文档图像中将其分成互相重叠的水平条带进行分割，本网络得到了优秀的效果，我们还新建了人工标注的文档表单数据集并在此数据集上进行实验，对比不同分割 baseline 方法证明了本网络算法在分层结构提取上的高效性。当前，此算法已用于 Adobe 的 AEM Forms，实现了纸质和 PDF 表单的自动转换为现代 HTML 表单。