Broad-Coverage Semantic Parsing as Transduction

By Sheng Zhang, Xutai Ma, Kevin Duh, Benjamin Van Durme. 2019

74 citations recorded by the source corpus.

We unify different broad-coverage semantic parsing tasks into a transduction parsing paradigm, and propose an attention-based neural transducer that incrementally builds meaning representation via a sequence of semantic relations. By leveraging multiple attention mechanisms, the neural transducer can be effectively trained without relying on a pre-trained aligner. Experiments separately conducted on three broad-coverage semantic parsing tasks – AMR, SDP and UCCA – demonstrate that our attention-based neural transducer improves the state of the art on both AMR and UCCA, and is competitive with the state of the art on SDP.

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. Lizhen Qu
2. Shuo Huang
3. Gholamreza Haffari
4. Zhuang Li

In Computer Vision and Pattern Recognition

1. Lizhen Qu
2. Shuo Huang
3. Gholamreza Haffari
4. Zhuang Li

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).”
• 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 Neubig, 2018) is SEQ2SEQ with attention (Bahdanau et al.”
• A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering (2020, unknown): “[41]), as well as systems for sentence level semantic parsing [64].”