StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing

By Pengcheng Yin, Chunting Zhou, Junxian He, Graham Neubig. 2018

103 citations recorded by the source corpus.

Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. Annotating NL utterances with their corresponding MRs is expensive and time-consuming, and thus the limited availability of labeled data often becomes the bottleneck of data-driven, supervised models. We introduce StructVAE, a variational auto-encoding model for semi-supervised semantic parsing, which learns both from limited amounts of parallel data, and readily-available unlabeled NL utterances. StructVAE models latent MRs not observed in the unlabeled data as tree-structured latent variables. Experiments on semantic parsing on the ATIS domain and Python code generation show that with extra unlabeled data, StructVAE outperforms strong supervised models.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Revisiting Self-Training for Neural Sequence Generation (2019, methodology): “…et al., 2015) makes use of the monolingual data on the target side to improve machine translation systems, latent variable models (Kingma et al., 2014) are employed to incorporate unlabeled source data to facilitate sentence compression (Miao & Blunsom, 2016) or code generation (Yin et al., 2018).”
Meta-Sim2: Unsupervised Learning of Scene Structure for Synthetic Data Generation (2020, methodology): “Grammar based methods have been used for a variety of tasks such as program translation [9], conditional program generation [71,72], grammar induction [32] and generative modelling on structures with syntax [37,12], such as molecules.”
Unsupervised Recurrent Neural Network Grammars (2019, methodology): “This control variate worked better than alternatives such as estimates of baselines from an auxiliary network (Mnih and Gregor, 2014; Deng et al., 2018) or a language model (Yin et al., 2018).”
Complexity-Based Prompting for Multi-Step Reasoning (2022, background): “The practice of sampling then voting is also similar to marginalizing out semantic parses (Yin et al., 2018).”
Program Synthesis and Semantic Parsing with Learned Code Idioms (2019, methodology): “There has been a lot of recent interest in applying recurrent sequence-based and tree-based neural networks to semantic parsing [11, 18, 21, 38, 40].”
Semantic Parsing with Dual Learning (2019, methodology): “Yin et al. (2018) introduce a variational auto-encoding model for semi-supervised semantic parsing.”
Latent Relation Language Models (2019, background): “…and Neu-big, 2018), deciding between input sources (Ling et al., 2016; Gu et al., 2016), predicting latent continuous vectors (Bowman et al., 2016), generating compressed summary tokens (Miao and Blun-som, 2016), or inducing syntactic and semantic trees (Yogatama et al., 2016; Yin et al., 2018).”
Few-Shot Semantic Parsing for New Predicates (2021, methodology): “The supervised attention (Rabinovich et al., 2017; Yin and Neubig, 2018) is also applied to facilitate the learning of attention weights.”
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem (2020, background): “…decoders, including tree decoders (Dong and Lapata, 2016; Alvarez-Melis and Jaakkola, 2017), grammar constrained decoders (Yin and Neubig, 2017; Yin et al., 2018; Jie and Lu, 2018; Dong and Lapata, 2018), action sequences for semantic graph generation (Chen et al., 2018a), and modular decoders…”