# Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing

arXiv: https://arxiv.org/abs/2110.00621 · HTML: https://mlsanity.com/paper/2110.00621/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 74 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Multilingual Constituency Parsing with Self-Attention and Pre-Training](https://mlsanity.com/paper/1812.11760/) (2018)
- [Neural AMR: Sequence-to-Sequence Models for Parsing and Generation](https://mlsanity.com/paper/1704.08381/) (2017)
- [AMR Parsing as Sequence-to-Graph Transduction](https://mlsanity.com/paper/1905.08704/) (2019)
- [AMR Parsing via Graph-Sequence Iterative Inference](https://mlsanity.com/paper/2004.05572/) (2020)
- [Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing](https://mlsanity.com/paper/1909.06775/) (2019)
- [Toward Abstractive Summarization Using Semantic Representations](https://mlsanity.com/paper/1805.10399/) (2018)
- [The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations](https://mlsanity.com/paper/1702.03964/) (2017)
- [Semantic Neural Machine Translation using AMR](https://mlsanity.com/paper/1902.07282/) (2019)
- [From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers](https://mlsanity.com/paper/2005.00633/) (2020)
- [Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies](https://mlsanity.com/paper/1905.05475/) (2019)
- [Second-Order Semantic Dependency Parsing with End-to-End Neural Networks](https://mlsanity.com/paper/1906.07880/) (2019)
- [Simple and Effective Text Simplification Using Semantic and Neural Methods](https://mlsanity.com/paper/1810.05104/) (2018)
- [Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations](https://mlsanity.com/paper/1910.05479/) (2019)
- [A Transition-Based Directed Acyclic Graph Parser for UCCA](https://mlsanity.com/paper/1704.00552/) (2017)
- [Robust Incremental Neural Semantic Graph Parsing](https://mlsanity.com/paper/1704.07092/) (2017)
- [Text Summarization using Abstract Meaning Representation](https://mlsanity.com/paper/1706.01678/) (2017)
- ['UFAL at MRP 2020: Permutation-invariant Semantic Parsing in PERIN](https://mlsanity.com/paper/2011.00758/) (2020)
- [Addressing the Data Sparsity Issue in Neural AMR Parsing](https://mlsanity.com/paper/1702.05053/) (2017)
- [Deep Multitask Learning for Semantic Dependency Parsing](https://mlsanity.com/paper/1704.06855/) (2017)
- [Multitask Parsing Across Semantic Representations](https://mlsanity.com/paper/1805.00287/) (2018)
- [SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA](https://mlsanity.com/paper/1903.02953/) (2019)
- [HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing](https://mlsanity.com/paper/1903.04153/) (2019)
- [Hitachi at MRP 2019: Unified Encoder-to-Biaffine Network for Cross-Framework Meaning Representation Parsing](https://mlsanity.com/paper/1910.01299/) (2019)
- ['UFAL MRPipe at MRP 2019: UDPipe Goes Semantic in the Meaning Representation Parsing Shared Task](https://mlsanity.com/paper/1910.11295/) (2019)
- [HUJI-KU at MRP~2020: Two Transition-based Neural Parsers](https://mlsanity.com/paper/2010.05710/) (2020)
- [MaskParse@Deskin at SemEval-2019 Task 1: Cross-lingual UCCA Semantic Parsing using Recursive Masked Sequence Tagging](https://mlsanity.com/paper/1910.02733/) (2019)

## 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.