In this work, we investigate the problems of semantic parsing in a few-shot
learning setting. In this setting, we are provided with utterance-logical form
pairs per new predicate. The state-of-the-art neural semantic parsers achieve
less than 25% accuracy on benchmark datasets when k= 1. To tackle this problem,
we proposed to i) apply a designated meta-learning method to train the model;
ii) regularize attention scores with alignment statistics; iii) apply a
smoothing technique in pre-training. As a result, our method consistently
outperforms all the baselines in both one and two-shot settings.

本研究探讨了少样本学习条件下语义解析的问题，提出了应用元学习方法进行模型训练、用对齐统计规则化注意力得分、在预训练中应用平滑技术等方法，显著提高了模型准确度。

新谓词的少样本语义解析

Few-Shot Semantic Parsing for New Predicates

Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a
sequential LSTM, to extract word order features while neglecting other valuable
syntactic information such as dependency graph or constituent trees. In this
paper, we first propose to use the \textit{syntactic graph} to represent three
types of syntactic information, i.e., word order, dependency and constituency
features. We further employ a graph-to-sequence model to encode the syntactic
graph and decode a logical form. Experimental results on benchmark datasets
show that our model is comparable to the state-of-the-art on Jobs640, ATIS and
Geo880. Experimental results on adversarial examples demonstrate the robustness
of the model is also improved by encoding more syntactic information.

本文提出了使用句法图来表示三种句法信息（单词顺序，依赖关系和从属关系）的方法，并使用图到序列模型对句法图进行编码和解码逻辑形式。基准数据集上的实验结果表明，该模型与 Jobs640、ATIS 和 Geo880 的最新研究成果相当。对抗示例上的实验结果表明，通过编码更多的句法信息，该模型的鲁棒性也得到了提高。

利用丰富的句法信息进行图序列模型的语义解析

Exploiting Rich Syntactic Information for Semantic Parsing with  Graph-to-Sequence Model

In this work we focus on confidence modeling for neural semantic parsers
which are built upon sequence-to-sequence models. We outline three major causes
of uncertainty, and design various metrics to quantify these factors. These
metrics are then used to estimate confidence scores that indicate whether model
predictions are likely to be correct. Beyond confidence estimation, we identify
which parts of the input contribute to uncertain predictions allowing users to
interpret their model, and verify or refine its input. Experimental results
show that our confidence model significantly outperforms a widely used method
that relies on posterior probability, and improves the quality of
interpretation compared to simply relying on attention scores.

本研究致力于神经语义分析器的置信度建模，设计了各种度量标准来估计置信分数和确定哪些输入因素会导致不确定的预测，结论表明我们的模型显著优于传统方法并且提高了模型解释的质量。