We present ABEX, a novel and effective generative data augmentation
methodology for low-resource Natural Language Understanding (NLU) tasks. ABEX
is based on ABstract-and-EXpand, a novel paradigm for generating diverse forms
of an input document -- we first convert a document into its concise, abstract
description and then generate new documents based on expanding the resultant
abstraction. To learn the task of expanding abstract descriptions, we first
train BART on a large-scale synthetic dataset with abstract-document pairs.
Next, to generate abstract descriptions for a document, we propose a simple,
controllable, and training-free method based on editing AMR graphs. ABEX brings
the best of both worlds: by expanding from abstract representations, it
preserves the original semantic properties of the documents, like style and
meaning, thereby maintaining alignment with the original label and data
distribution. At the same time, the fundamental process of elaborating on
abstract descriptions facilitates diverse generations. We demonstrate the
effectiveness of ABEX on 4 NLU tasks spanning 12 datasets and 4 low-resource
settings. ABEX outperforms all our baselines qualitatively with improvements of
0.04% - 38.8%. Qualitatively, ABEX outperforms all prior methods from
literature in terms of context and length diversity.

我们提出了一种新颖有效的 ABEX 生成数据增强方法，用于低资源自然语言理解（NLU）任务。 ABEX 基于 ABstract-and-EXpand，一种生成输入文档不同形式的新颖范例，我们首先将文档转换为简洁、抽象的描述，然后根据扩展所得到的抽象生成新的文档。我们证明了 ABEX 在涵盖 12 个数据集和 4 个低资源情况下的 4 个 NLU 任务上的有效性，在质量上超过了所有基线，并获得了从上一文献中以各种不同的上下文和长度多样性而言的最佳性能。

ABEX: 通过扩展抽象描述进行低资源 NLU 的数据增强

ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract  Descriptions

In task and motion planning (TAMP), the ambiguity and underdetermination of
abstract descriptions used by task planning methods make it difficult to
characterize physical constraints needed to successfully execute a task. The
usual approach is to overlook such constraints at task planning level and to
implement expensive sub-symbolic geometric reasoning techniques that perform
multiple calls on unfeasible actions, plan corrections, and re-planning until a
feasible solution is found. We propose an alternative TAMP approach that
unifies task and motion planning into a single heuristic search. Our approach
is based on an object-centric abstraction of motion constraints that permits
leveraging the computational efficiency of off-the-shelf AI heuristic search to
yield physically feasible plans. These plans can be directly transformed into
object and motion parameters for task execution without the need of intensive
sub-symbolic geometric reasoning.

任务和动作规划方法中的抽象描述模糊和欠定性使得很难确定成功执行任务所需的物理约束。我们提出了一种替代的任务和动作规划方法，将任务和动作规划融合为一个启发式搜索，并基于基于物体的抽象的运动约束，从而利用现成的人工智能启发式搜索的计算效率，产生具有物理可行性的计划，无需进行密集的子符号几何推理。