Since the meaning representations are detailed and accurate annotations which
express fine-grained sequence-level semtantics, it is usually hard to train
discriminative semantic parsers via Maximum Likelihood Estimation (MLE) in an
autoregressive fashion. In this paper, we propose a semantic-aware contrastive
learning algorithm, which can learn to distinguish fine-grained meaning
representations and take the overall sequence-level semantic into
consideration. Specifically, a multi-level online sampling algorithm is
proposed to sample confusing and diverse instances. Three semantic-aware
similarity functions are designed to accurately measure the distance between
meaning representations as a whole. And a ranked contrastive loss is proposed
to pull the representations of the semantic-identical instances together and
push negative instances away. Experiments on two standard datasets show that
our approach achieves significant improvements over MLE baselines and gets
state-of-the-art performances by simply applying semantic-aware contrastive
learning on a vanilla Seq2Seq model.

本文提出一种基于语义的对比学习算法，旨在从多层次角度区分微观含义表达，并结合整体序列级的语义。该算法采用多级在线抽样算法，在采样数据时考虑到差异化的实例。文中更提出三种基于语义感知的相似度函数，并使用排名对比损失来拉近相同语义示例的表示并将负样本案例里散开，实验结果表明，本文的算法在两个标准数据集上都取得了显著的改进，并只是在基本 Seq2Seq 模型上应用了基于语义的对比学习，使模型性能达到了最佳状态。