Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 74 references that are in the map. First posted on arXiv 1 Oct 2021.
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References · 31
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19 more
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