# Cross-lingual Semantic Role Labeling with Model Transfer

arXiv: https://arxiv.org/abs/2008.10284 · HTML: https://mlsanity.com/paper/2008.10284/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

## Built on by (most cited first, 11 of 11)

- [Gradual Fine-Tuning for Low-Resource Domain Adaptation](https://mlsanity.com/paper/2103.02205/) (2021)
- [End-to-end Semantic Role Labeling with Neural Transition-based Model](https://mlsanity.com/paper/2101.00394/) (2021)
- [Constructing Holistic Spatio-Temporal Scene Graph for Video Semantic Role Labeling](https://mlsanity.com/paper/2308.05081/) (2023)
- [Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition](https://mlsanity.com/paper/2308.04502/) (2023)
- [Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction](https://mlsanity.com/paper/2305.12258/) (2023)
- [A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition](https://mlsanity.com/paper/2308.04424/) (2023)
- [DialogRE^C+: An Extension of DialogRE to Investigate How Much Coreference Helps Relation Extraction in Dialogs](https://mlsanity.com/paper/2308.04498/) (2023)
- [Zero-shot Cross-lingual Conversational Semantic Role Labeling](https://mlsanity.com/paper/2204.04914/) (2022)
- [Leveraging Semantic Representations Combined with Contextual Word Representations for Recognizing Textual Entailment in Vietnamese](https://mlsanity.com/paper/2301.00422/) (2023)
- [Dynamic Syntax Mapping: A New Approach to Unsupervised Syntax Parsing](https://mlsanity.com/paper/2312.14966/) (2023)
- [A New Method for Cross-Lingual-based Semantic Role Labeling](https://mlsanity.com/paper/2408.15896/) (2024)

## Abstract (arXiv)

Prior studies show that cross-lingual semantic role labeling (SRL) can be achieved by model transfer under the help of universal features. In this paper, we fill the gap of cross-lingual SRL by proposing an end-to-end SRL model that incorporates a variety of universal features and transfer methods. We study both the bilingual transfer and multi-source transfer, under gold or machine-generated syntactic inputs, pre-trained high-order abstract features, and contextualized multilingual word representations. Experimental results on the Universal Proposition Bank corpus indicate that performances of the cross-lingual SRL can vary by leveraging different cross-lingual features. In addition, whether the features are gold-standard also has an impact on performances. Precisely, we find that gold syntax features are much more crucial for cross-lingual SRL, compared with the automatically-generated ones. Moreover, universal dependency structure features are able to give the best help, and both pre-trained high-order features and contextualized word representations can further bring significant improvements.