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.

本研究提出一种半自动化框架，使用现有的表格数据重新解构，构建表格自然语言推理实例，以提高性能，分析零样本情况下在不同重新解构数据集类型的表现趋势。

利用数据重塑以增强表格推理

Leveraging Data Recasting to Enhance Tabular Reasoning

A comprehensive semantic understanding of a scene is important for many
applications - but in what space should diverse semantic information (e.g.,
objects, scene categories, material types, texture, etc.) be grounded and what
should be its structure? Aspiring to have one unified structure that hosts
diverse types of semantics, we follow the Scene Graph paradigm in 3D,
generating a 3D Scene Graph. Given a 3D mesh and registered panoramic images,
we construct a graph that spans the entire building and includes semantics on
objects (e.g., class, material, and other attributes), rooms (e.g., scene
category, volume, etc.) and cameras (e.g., location, etc.), as well as the
relationships among these entities.
However, this process is prohibitively labor heavy if done manually. To
alleviate this we devise a semi-automatic framework that employs existing
detection methods and enhances them using two main constraints: I. framing of
query images sampled on panoramas to maximize the performance of 2D detectors,
and II. multi-view consistency enforcement across 2D detections that originate
in different camera locations.

本研究提出了一种基于场景图的三维语义理解方法，构建了一个场景图，包括物体、房间和摄像机之间的关系，同时也提出了一种半自动的框架以减少手动处理的工作量，并增强了现有的检测方法，通过查询图片和多视角一致性实现了更好的检测表现。