Semantic parsing plays a key role in digital voice assistants such as Alexa, Siri, and Google Assistant by mapping natural language to structured meaning representations. When we want to improve the capabilities of a voice assistant by adding a new domain, the underlying semantic parsing model needs to be retrained using thousands of annotated examples from the new domain, which is time-consuming and expensive. In this work, we present an architecture to perform such domain adaptation automatically, with only a small amount of metadata about the new domain and without any new training data (zero-shot) or with very few examples (few-shot). We use a base seq2seq (sequence-to-sequence) architecture and augment it with a concept encoder that encodes intent and slot tags from the new domain. We also introduce a novel decoder-focused approach to pretrain seq2seq models to be concept aware using Wikidata and use it to help our model learn important concepts and perform well in low-resource settings. We report few-shot and zero-shot results for compositional semantic parsing on the TOPv2 dataset and show that our model outperforms prior approaches in few-shot settings for the TOPv2 and SNIPS datasets.

翻译：语义解析在Alexa、Siri和Google Assistant等数字语音助手中发挥关键作用，通过将自然语言映射到结构化语义表示。当我们需要通过添加新领域来提升语音助手能力时，底层语义解析模型需要利用新领域的数千条标注示例重新训练，这一过程耗时且成本高昂。本文提出一种可自动完成此类领域适应的架构，仅需新领域的少量元数据，无需任何新训练数据（零样本）或仅需极少量示例（少样本）。我们采用基础序列到序列（seq2seq）架构，并为其增加一个概念编码器，该编码器可编码新领域中的意图和槽位标签。同时引入一种新颖的以解码器为中心的预训练方法，通过维基数据使seq2seq模型具备概念感知能力，从而帮助模型学习重要概念并在低资源场景下表现优异。我们在TOPv2数据集上报告了组合语义解析的少样本和零样本结果，并证明在TOPv2和SNIPS数据集的少样本场景中，我们的模型优于现有方法。