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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Semantic-aware Contrastive Learning for More Accurate Semantic Parsing
%A Wu, Shan
%A Xin, Chunlei
%A Chen, Bo
%A Han, Xianpei
%A Sun, Le
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F wu-etal-2022-semantic
%X 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.
%R 10.18653/v1/2022.emnlp-main.269
%U https://aclanthology.org/2022.emnlp-main.269
%U https://doi.org/10.18653/v1/2022.emnlp-main.269
%P 4040-4052

Markdown (Informal)

[Semantic-aware Contrastive Learning for More Accurate Semantic Parsing](https://aclanthology.org/2022.emnlp-main.269) (Wu et al., EMNLP 2022)

• Semantic-aware Contrastive Learning for More Accurate Semantic Parsing (Wu et al., EMNLP 2022)

ACL

• Shan Wu, Chunlei Xin, Bo Chen, Xianpei Han, and Le Sun. 2022. Semantic-aware Contrastive Learning for More Accurate Semantic Parsing. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4040–4052, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.