merterm/intensified-phoenix-14-t

这是一个德语到德语手语（DGS）的天气预报数据集，是一个对RWTH-PHOENIX-Weather-2014T数据集进行韵律增强的版本。数据集由Mert Inan精心策划，包含德语文本、DGS术语和DGS骨骼坐标的并行样本，采用OpenPose格式。该数据集主要用于手语生成，特别是在一篇关于手语生成中强化建模的计算方法的论文中使用。

原始信息汇总

Intensified PHOENIX 14-T German Sign Language Dataset

数据集详情

数据集描述

• 数据集名称： Intensified PHOENIX 14-T German Sign Language Dataset
• 数据集简介： 这是一个德语到德语手语（DGS）的天气预报数据集，是RWTH-PHOENIX-Weather-2014T数据集的韵律增强版本。
• 语言： 德语，DGS（德语手语）
• 数据集大小： 1K<n<10K

数据集用途

• 主要用途： 用于手语生成研究，数据包含德语、德语手语（DGS）词汇和德语手语（DGS）骨骼坐标（OpenPose格式，不包括面部）的并行样本。

数据集来源

• 论文： Modeling Intensification for Sign Language Generation: A Computational Approach @ ACL 2022
• 代码仓库： Modeling Intensification for Sign Language Generation

数据集创建者

• 创建者： [Mert Inan]

引用信息

• BibTeX： bibtex @inproceedings{inan-etal-2022-modeling, title = "Modeling Intensification for Sign Language Generation: A Computational Approach", author = "Inan, Mert and Zhong, Yang and Hassan, Sabit and Quandt, Lorna and Alikhani, Malihe", editor = "Muresan, Smaranda and Nakov, Preslav and Villavicencio, Aline", booktitle = "Findings of the Association for Computational Linguistics: ACL 2022", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.findings-acl.228", doi = "10.18653/v1/2022.findings-acl.228", pages = "2897--2911", abstract = "End-to-end sign language generation models do not accurately represent the prosody in sign language. A lack of temporal and spatial variations leads to poor-quality generated presentations that confuse human interpreters. In this paper, we aim to improve the prosody in generated sign languages by modeling intensification in a data-driven manner. We present different strategies grounded in linguistics of sign language that inform how intensity modifiers can be represented in gloss annotations. To employ our strategies, we first annotate a subset of the benchmark PHOENIX-14T, a German Sign Language dataset, with different levels of intensification. We then use a supervised intensity tagger to extend the annotated dataset and obtain labels for the remaining portion of it. This enhanced dataset is then used to train state-of-the-art transformer models for sign language generation. We find that our efforts in intensification modeling yield better results when evaluated with automatic metrics. Human evaluation also indicates a higher preference of the videos generated using our model.", }

搜集汇总

背景与挑战

背景概述

该数据集是一个德语到德语手语的天气预测数据集，包含德语文本、手语注释和骨骼坐标的并行样本，主要用于改进手语生成模型的韵律表现。