metaeval/autotnli

---
license: apache-2.0
language:
- en
task_ids:
- natural-language-inference
task_categories:
- text-classification
---
https://github.com/Dibyakanti/AutoTNLI-code
```
@inproceedings{kumar-etal-2022-autotnli,
title = "Realistic Data Augmentation Framework for Enhancing Tabular Reasoning",
author = "Kumar, Dibyakanti and
Gupta, Vivek and
Sharma, Soumya and
Zhang, Shuo",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Online and Abu Dhabi",
publisher = "Association for Computational Linguistics",
url = "https://vgupta123.github.io/docs/autotnli.pdf",
pages = "",
abstract = "Existing approaches to constructing training data for Natural Language Inference (NLI) tasks, such as for semi-structured table reasoning, are either via crowdsourcing or fully automatic methods. However, the former is expensive and time-consuming and thus limits scale, and the latter often produces naive examples that may lack complex reasoning. This paper develops a realistic semi-automated framework for data augmentation for tabular inference. Instead of manually generating a hypothesis for each table, our methodology generates hypothesis templates transferable to similar tables. In addition, our framework entails the creation of rational counterfactual tables based on human written logical constraints and premise paraphrasing. For our case study, we use the InfoTabS (Gupta et al., 2020), which is an entity-centric tabular inference dataset. We observed that our framework could generate human-like tabular inference examples, which could benefit training data augmentation, especially in the scenario with limited supervision.",
}
```

原始信息汇总

数据集概述

基本信息

• 许可证: Apache-2.0
• 语言: 英语

任务相关信息

• 任务ID: 自然语言推理
• 任务类别: 文本分类

数据集描述

• 标题: Realistic Data Augmentation Framework for Enhancing Tabular Reasoning
• 作者: Kumar, Dibyakanti; Gupta, Vivek; Sharma, Soumya; Zhang, Shuo
• 出版信息: 2022 Conference on Empirical Methods in Natural Language Processing
• 地点: 在线和阿布扎比
• 出版者: Association for Computational Linguistics
• 摘要: 本研究提出了一种半自动化的数据增强框架，用于增强表格推理的自然语言推理任务。该框架通过生成可转移至类似表格的假设模板，并创建基于人类编写的逻辑约束和前提改写的合理反事实表格，来生成类似人类的表格推理示例。此方法特别适用于监督有限的情况下的训练数据增强。