# Cross-domain Generalization for AMR Parsing

arXiv: https://arxiv.org/abs/2210.12445 · HTML: https://mlsanity.com/paper/2210.12445/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 51 of its 86 references that are in the map.
- First posted on arXiv 22 Oct 2022.

## Builds on (most cited first, 30 of 51)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
- [Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2010.00710/) (2020)
- [DialogSum: A Real-Life Scenario Dialogue Summarization Dataset](https://mlsanity.com/paper/2105.06762/) (2021)
- [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021)
- [Investigating Pretrained Language Models for Graph-to-Text Generation](https://mlsanity.com/paper/2007.08426/) (2020)
- [Graph Transformer for Graph-to-Sequence Learning](https://mlsanity.com/paper/1911.07470/) (2019)
- [Template-free Prompt Tuning for Few-shot NER](https://mlsanity.com/paper/2109.13532/) (2021)
- [Neural AMR: Sequence-to-Sequence Models for Parsing and Generation](https://mlsanity.com/paper/1704.08381/) (2017)
- [A Graph-to-Sequence Model for AMR-to-Text Generation](https://mlsanity.com/paper/1805.02473/) (2018)
- [Graph Pre-training for AMR Parsing and Generation](https://mlsanity.com/paper/2203.07836/) (2022)
- [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)
- [Toward Abstractive Summarization Using Semantic Representations](https://mlsanity.com/paper/1805.10399/) (2018)
- [Transition-based Parsing with Stack-Transformers](https://mlsanity.com/paper/2010.10669/) (2020)
- [Semantic Neural Machine Translation using AMR](https://mlsanity.com/paper/1902.07282/) (2019)
- [AMR Parsing as Graph Prediction with Latent Alignment](https://mlsanity.com/paper/1805.05286/) (2018)
- [Abstract Meaning Representation for Multi-Document Summarization](https://mlsanity.com/paper/1806.05655/) (2018)
- [Modeling Graph Structure in Transformer for Better AMR-to-Text Generation](https://mlsanity.com/paper/1909.00136/) (2019)
- [An Incremental Parser for Abstract Meaning Representation](https://mlsanity.com/paper/1608.06111/) (2016)
- [WebFormer: The Web-page Transformer for Structure Information Extraction](https://mlsanity.com/paper/2202.00217/) (2022)
- [Broad-Coverage Semantic Parsing as Transduction](https://mlsanity.com/paper/1909.02607/) (2019)
- [Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks](https://mlsanity.com/paper/1703.06345/) (2017)
- [AdaPrompt: Adaptive Model Training for Prompt-based NLP](https://mlsanity.com/paper/2202.04824/) (2022)
- [Maximum Bayes Smatch Ensemble Distillation for AMR Parsing](https://mlsanity.com/paper/2112.07790/) (2021)
- [Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization](https://mlsanity.com/paper/2208.09770/) (2022)

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

- [World Models for Math Story Problems](https://mlsanity.com/paper/2306.04347/) (2023)
- [Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions](https://mlsanity.com/paper/2302.03950/) (2023)

## Abstract (arXiv)

Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph from textual input. Recently, there has been notable growth in AMR parsing performance. However, most existing work focuses on improving the performance in the specific domain, ignoring the potential domain dependence of AMR parsing systems. To address this, we extensively evaluate five representative AMR parsers on five domains and analyze challenges to cross-domain AMR parsing. We observe that challenges to cross-domain AMR parsing mainly arise from the distribution shift of words and AMR concepts. Based on our observation, we investigate two approaches to reduce the domain distribution divergence of text and AMR features, respectively. Experimental results on two out-of-domain test sets show the superiority of our method.