Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing

By Bo Chen, Xianpei Han. 2018

85 citations recorded by the source corpus.

This paper proposes a neural semantic parsing approach – Sequence-to-Action, which models semantic parsing as an end-to-end semantic graph generation process. Our method simultaneously leverages the advantages from two recent promising directions of semantic parsing. Firstly, our model uses a semantic graph to represent the meaning of a sentence, which has a tight-coupling with knowledge bases. Secondly, by leveraging the powerful representation learning and prediction ability of neural network models, we propose a RNN model which can effectively map sentences to action sequences for semantic graph generation. Experiments show that our method achieves state-of-the-art performance on Overnight dataset and gets competitive performance on Geo and Atis datasets.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Philip S. Yu
2. Duyu Tang
3. Nan Duan
4. M. Zhou
5. Daya Guo
6. Lichao Sun
7. Lingfei Wu
8. Jian Yin

In Computer Vision and Pattern Recognition

1. Nan Duan
2. Philip S. Yu
3. Giovanni Campagna
4. Qian Liu
5. M. Zhou
6. Su Zhu
7. Jian Yin
8. Feng Jiang

Citations in the corpus

• Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers (2021, background): “, 2019) or sequences of parse actions (Chen et al., 2018; Cheng et al., 2019; Lin et al., 2019; Zhang et al., 2019; Yin and Neubig, 2018; Chen et al., 2018; Guo et al., 2019; Wang et al., 2020a; Li et al., 2021).”
• Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing (2019, background): “Sequence-to-Action approaches (Yin and Neubig, 2017; Krish-namurthy et al., 2017; Iyyer et al., 2017; Chen et al., 2018) treat semantic parsing as the prediction of a action sequence that can construct logical forms, which not only guarantee the grammatical correctness of outputs, but also leverage…”
• Few-Shot Semantic Parsing for New Predicates (2021, methodology): “At the core of most recent models (Chen et al., 2018; Cheng et al., 2019; Lin et al., 2019; Zhang et al., 2019b; Yin and Neu-big, 2018) is S EQ 2S EQ with attention (Bahdanau et al., 2014) by formulating the task as a machine translation problem.”
• A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT (2023, background): “[185] construct semantic graph with an action set via a neural sequence-to-action RNN model.”
• ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering (2021, methodology): “Inspired by previous work (Liang et al., 2017; Chen et al., 2018; Wang et al., 2018), we design a plug-gable module named checker to improve the decoding process by leveraging semantics of KB. Instance-level Checking relies on the KB linkage information at the instance level (i.e., entities and…”
• Building a Neural Semantic Parser from a Domain Ontology (2018, unknown): “Cheng et al. (2017a, 2017b), Yin and Neubig (2018), Gupta et al. (2018), Chen et al. (2018) all employ neural sequence-to-action models to generate structured meaning representations.”
• AutoQA: From Databases to Q&A Semantic Parsers with Only Synthetic Training Data (2020, methodology): “For reference, we also include two of the best-performing models that use in-domain human data (Cao et al., 2019; Chen et al., 2018)4.”
• Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem (2020, background): “, 2018; Jie and Lu, 2018; Dong and Lapata, 2018), action sequences for semantic graph generation (Chen et al., 2018a), and modular decoders based on abstract syntax trees (Rabinovich et al.”
• Semantic Parsing with Dual Learning (2019, methodology): “Yin and Neubig (2017) and Chen et al. (2018a) both transform the generation of logical forms into query graph construction.”