论文标题
利用重新铸造的数据来增强表格推理
Leveraging Data Recasting to Enhance Tabular Reasoning
论文作者
论文摘要
创建挑战性的表格推理数据对于学习复杂的推理至关重要。先前的工作主要依赖两种数据生成策略。第一个是人类注释，它产生了语言上不同的数据，但很难扩展。创建的第二类是合成生成，它是可扩展的且具有成本效益的，但缺乏创造力。在这项研究中，我们提出了一个半自动重塑现有表格数据的框架，以利用两种方法的好处。我们利用框架从最初用于诸如Table2Text创建，表格Q/A和语义解析之类的任务的五个数据集中构建表格NLI实例。我们证明，重铸数据可以用作评估基准和增强数据，以增强表格NLI任务的性能。此外，我们研究了在零拍摄方案中对重铸数据训练的模型的有效性，并分析了不同重铸数据集类型的性能趋势。
Creating challenging tabular inference data is essential for learning complex reasoning. Prior work has mostly relied on two data generation strategies. The first is human annotation, which yields linguistically diverse data but is difficult to scale. The second category for creation is synthetic generation, which is scalable and cost effective but lacks inventiveness. In this research, we present a framework for semi-automatically recasting existing tabular data to make use of the benefits of both approaches. We utilize our framework to build tabular NLI instances from five datasets that were initially intended for tasks like table2text creation, tabular Q/A, and semantic parsing. We demonstrate that recasted data could be used as evaluation benchmarks as well as augmentation data to enhance performance on tabular NLI tasks. Furthermore, we investigate the effectiveness of models trained on recasted data in the zero-shot scenario, and analyse trends in performance across different recasted datasets types.
