Task-oriented semantic parsing models typically have high resource
requirements: to support new ontologies (i.e., intents and slots),
practitioners crowdsource thousands of samples for supervised fine-tuning.
Partly, this is due to the structure of de facto copy-generate parsers; these
models treat ontology labels as discrete entities, relying on parallel data to
extrinsically derive their meaning. In our work, we instead exploit what we
intrinsically know about ontology labels; for example, the fact that
SL:TIME_ZONE has the categorical type "slot" and language-based span "time
zone". Using this motivation, we build our approach with offline and online
stages. During preprocessing, for each ontology label, we extract its intrinsic
properties into a component, and insert each component into an inventory as a
cache of sorts. During training, we fine-tune a seq2seq, pre-trained
transformer to map utterances and inventories to frames, parse trees comprised
of utterance and ontology tokens. Our formulation encourages the model to
consider ontology labels as a union of its intrinsic properties, therefore
substantially bootstrapping learning in low-resource settings. Experiments show
our model is highly sample efficient: using a low-resource benchmark derived
from TOPv2, our inventory parser outperforms a copy-generate parser by +15 EM
absolute (44% relative) when fine-tuning on 10 samples from an unseen domain.

本研究提出了一种基于任务的语义解析模型来提高在低资源环境下的学习效率，并通过预处理和训练过程探索了本体标签的内在属性，以作为知识库，最终实现了在模型效率和性能方面的提升。

通过固有建模实现低资源任务导向语义解析

Low-Resource Task-Oriented Semantic Parsing via Intrinsic Modeling

The ability to edit materials of objects in images is desirable by many
content creators. However, this is an extremely challenging task as it requires
to disentangle intrinsic physical properties of an image. We propose an
end-to-end network architecture that replicates the forward image formation
process to accomplish this task. Specifically, given a single image, the
network first predicts intrinsic properties, i.e. shape, illumination, and
material, which are then provided to a rendering layer. This layer performs
in-network image synthesis, thereby enabling the network to understand the
physics behind the image formation process. The proposed rendering layer is
fully differentiable, supports both diffuse and specular materials, and thus
can be applicable in a variety of problem settings. We demonstrate a rich set
of visually plausible material editing examples and provide an extensive
comparative study.

本文提供了一种端到端的网络架构，旨在解决图像物体材料编辑的难题，提出了一个可完全微分的渲染层，支持漫反射和镜面材料，在比较研究中演示了一系列视觉上令人信服的材料编辑示例。