The dominant paradigm for semantic parsing in recent years is to formulate
parsing as a sequence-to-sequence task, generating predictions with
auto-regressive sequence decoders. In this work, we explore an alternative
paradigm. We formulate semantic parsing as a dependency parsing task, applying
graph-based decoding techniques developed for syntactic parsing. We compare
various decoding techniques given the same pre-trained Transformer encoder on
the TOP dataset, including settings where training data is limited or contains
only partially-annotated examples. We find that our graph-based approach is
competitive with sequence decoders on the standard setting, and offers
significant improvements in data efficiency and settings where
partially-annotated data is available.

本文探讨了一种新的语义分析方法，将其表述为依存句法分析任务，应用于为句法分析开发的基于图形的解码技术。实验相同的预训练变压器编码器在 TOP 数据集上，比较了不同的解码技术，包括训练数据有限或只包含部分注释示例的情况。结果表明，与序列解码器相比，我们的基于图形的方法在标准情况下具有竞争力，并在数据效率和可用部分注释数据的情况下提供了显着的改进。

面向任务的语义解析图解码

Graph-Based Decoding for Task Oriented Semantic Parsing

Traditional approaches to semantic parsing (SP) work by training individual
models for each available parallel dataset of text-meaning pairs. In this
paper, we explore the idea of polyglot semantic translation, or learning
semantic parsing models that are trained on multiple datasets and natural
languages. In particular, we focus on translating text to code signature
representations using the software component datasets of Richardson and Kuhn
(2017a,b). The advantage of such models is that they can be used for parsing a
wide variety of input natural languages and output programming languages, or
mixed input languages, using a single unified model. To facilitate modeling of
this type, we develop a novel graph-based decoding framework that achieves
state-of-the-art performance on the above datasets, and apply this method to
two other benchmark SP tasks.

通过训练多个数据集和自然语言的模型，实现多语言语义解析，将文本转换为代码标识，通过图形解码框架并在多个基准测试中取得了最优表现。