Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing
arXiv:2110.00621
Abstract
Semantic parsing provides a way to extract the semantic structure of a text that could be understood by machines. It is utilized in various NLP applications that require text comprehension such as summarization and question answering. Graph-based representation is one of the semantic representation approaches to express the semantic structure of a text. Such representations generate expressive and adequate graph-based target structures. In this paper, we focus primarily on UCCA graph-based semantic representation. The paper not only presents the existing approaches proposed for UCCA representation, but also proposes a novel self-attentive neural parsing model for the UCCA representation. We present the results for both single-lingual and cross-lingual tasks using zero-shot and few-shot learning for low-resource languages.
References in corpus (7)
- Sequence to Sequence Learning with Neural Networks
- Text Summarization using Abstract Meaning Representation
- Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies
- Hitachi at MRP 2019: Unified Encoder-to-Biaffine Network for Cross-Framework Meaning Representation Parsing
- HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing
- MaskParse@Deskin at SemEval-2019 Task 1: Cross-lingual UCCA Semantic Parsing using Recursive Masked Sequence Tagging
- ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing