# Holistic Exploration on Universal Decompositional Semantic Parsing: Architecture, Data Augmentation, and LLM Paradigm

arXiv: https://arxiv.org/abs/2307.13424 · HTML: https://mlsanity.com/paper/2307.13424/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 41 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://mlsanity.com/paper/1809.10185/) (2018)
- [Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling](https://mlsanity.com/paper/1703.04826/) (2017)
- [Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness](https://mlsanity.com/paper/2304.11633/) (2023)
- [ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark](https://mlsanity.com/paper/2303.13648/) (2023)
- [AMR Parsing as Sequence-to-Graph Transduction](https://mlsanity.com/paper/1905.08704/) (2019)
- [Broad-Coverage Semantic Parsing as Transduction](https://mlsanity.com/paper/1909.02607/) (2019)
- [Fine-Grained Temporal Relation Extraction](https://mlsanity.com/paper/1902.01390/) (2019)
- [Neural models of factuality](https://mlsanity.com/paper/1804.02472/) (2018)
- [Improving Simultaneous Machine Translation with Monolingual Data](https://mlsanity.com/paper/2212.01188/) (2022)
- [Multitask Parsing Across Semantic Representations](https://mlsanity.com/paper/1805.00287/) (2018)
- [Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements](https://mlsanity.com/paper/1901.11429/) (2019)
- [The Universal Decompositional Semantics Dataset and Decomp Toolkit](https://mlsanity.com/paper/1909.13851/) (2019)
- [Universal Decompositional Semantic Parsing](https://mlsanity.com/paper/1910.10138/) (2019)
- [Joint Universal Syntactic and Semantic Parsing](https://mlsanity.com/paper/2104.05696/) (2021)

## Built on by (most cited first, 2 of 2)

- [Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges](https://mlsanity.com/paper/2403.02990/) (2024)
- [NewTerm: Benchmarking Real-Time New Terms for Large Language Models with Annual Updates](https://mlsanity.com/paper/2410.20814/) (2024)

## Abstract (arXiv)

In this paper, we conduct a holistic exploration of the Universal Decompositional Semantic (UDS) Parsing. We first introduce a cascade model for UDS parsing that decomposes the complex parsing task into semantically appropriate subtasks. Our approach outperforms the prior models, while significantly reducing inference time. We also incorporate syntactic information and further optimized the architecture. Besides, different ways for data augmentation are explored, which further improve the UDS Parsing. Lastly, we conduct experiments to investigate the efficacy of ChatGPT in handling the UDS task, revealing that it excels in attribute parsing but struggles in relation parsing, and using ChatGPT for data augmentation yields suboptimal results. Our code is available at https://github.com/hexuandeng/HExp4UDS.