| Autor: |
Bölücü, Necva, Can, Burcu |
| Rok vydání: |
2021 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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