# End-to-end Semantic Role Labeling with Neural Transition-based Model

arXiv: https://arxiv.org/abs/2101.00394 · HTML: https://mlsanity.com/paper/2101.00394/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 50 references that are in the map.
- First posted on arXiv 2 Jan 2021.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling](https://mlsanity.com/paper/1703.04826/) (2017)
- [Linguistically-Informed Self-Attention for Semantic Role Labeling](https://mlsanity.com/paper/1804.08199/) (2018)
- [Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus](https://mlsanity.com/paper/2004.06295/) (2020)
- [Transition-Based Dependency Parsing with Stack Long Short-Term Memory](https://mlsanity.com/paper/1505.08075/) (2015)
- [Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling](https://mlsanity.com/paper/1805.04787/) (2018)
- [Core Semantic First: A Top-down Approach for AMR Parsing](https://mlsanity.com/paper/1909.04303/) (2019)
- [Dependency or Span, End-to-End Uniform Semantic Role Labeling](https://mlsanity.com/paper/1901.05280/) (2019)
- [Neural Semantic Role Labeling with Dependency Path Embeddings](https://mlsanity.com/paper/1605.07515/) (2016)
- [A Neural Transition-based Model for Nested Mention Recognition](https://mlsanity.com/paper/1810.01808/) (2018)
- [A Full End-to-End Semantic Role Labeler, Syntax-agnostic Over Syntax-aware?](https://mlsanity.com/paper/1808.03815/) (2018)
- [Cross-lingual Semantic Role Labeling with Model Transfer](https://mlsanity.com/paper/2008.10284/) (2020)
- [A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling](https://mlsanity.com/paper/1701.02593/) (2017)
- [Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies](https://mlsanity.com/paper/1906.01239/) (2019)
- [Syntax-aware Neural Semantic Role Labeling](https://mlsanity.com/paper/1907.09312/) (2019)
- [Syntax-Enhanced Self-Attention-Based Semantic Role Labeling](https://mlsanity.com/paper/1910.11204/) (2019)
- [Semantic Role Labeling with Iterative Structure Refinement](https://mlsanity.com/paper/1909.03285/) (2019)
- [High-order Refining for End-to-end Chinese Semantic Role Labeling](https://mlsanity.com/paper/2009.06957/) (2020)

## Built on by (most cited first, 22 of 22)

- [DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis](https://mlsanity.com/paper/2211.05705/) (2022)
- [Scene Graph as Pivoting: Inference-time Image-free Unsupervised Multimodal Machine Translation with Visual Scene Hallucination](https://mlsanity.com/paper/2305.12256/) (2023)
- [Mastering the Explicit Opinion-role Interaction: Syntax-aided Neural Transition System for Unified Opinion Role Labeling](https://mlsanity.com/paper/2110.02001/) (2021)
- [OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction](https://mlsanity.com/paper/2209.02693/) (2022)
- [Generating Visual Spatial Description via Holistic 3D Scene Understanding](https://mlsanity.com/paper/2305.11768/) (2023)
- [Cross2StrA: Unpaired Cross-lingual Image Captioning with Cross-lingual Cross-modal Structure-pivoted Alignment](https://mlsanity.com/paper/2305.12260/) (2023)
- [Constructing Holistic Spatio-Temporal Scene Graph for Video Semantic Role Labeling](https://mlsanity.com/paper/2308.05081/) (2023)
- [Conversational Semantic Role Labeling with Predicate-Oriented Latent Graph](https://mlsanity.com/paper/2210.03037/) (2022)
- [Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition](https://mlsanity.com/paper/2308.04502/) (2023)
- [Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction](https://mlsanity.com/paper/2209.04112/) (2022)
- [Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction](https://mlsanity.com/paper/2305.12258/) (2023)
- [Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures](https://mlsanity.com/paper/2212.01094/) (2022)
- [Transition-based Semantic Role Labeling with Pointer Networks](https://mlsanity.com/paper/2205.10023/) (2022)
- [ECQED: Emotion-Cause Quadruple Extraction in Dialogs](https://mlsanity.com/paper/2306.03969/) (2023)
- [TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2306.03974/) (2023)
- [Revisiting Conversation Discourse for Dialogue Disentanglement](https://mlsanity.com/paper/2306.03975/) (2023)
- [A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition](https://mlsanity.com/paper/2308.04424/) (2023)
- [Structured Sentiment Analysis as Transition-based Dependency Graph Parsing](https://mlsanity.com/paper/2305.05311/) (2023)
- [DialogRE^C+: An Extension of DialogRE to Investigate How Much Coreference Helps Relation Extraction in Dialogs](https://mlsanity.com/paper/2308.04498/) (2023)
- [LLMs Can Also Do Well! Breaking Barriers in Semantic Role Labeling via Large Language Models](https://mlsanity.com/paper/2506.05385/) (2025)
- [Revisiting Semantic Role Labeling: Efficient Structured Inference with Dependency-Informed Analysis](https://mlsanity.com/paper/2605.02505/) (2026)
- [Syntactic Fusion: Enhancing Aspect-Level Sentiment Analysis Through Multi-Tree Graph Integration](https://mlsanity.com/paper/2312.03738/) (2023)

## Abstract (arXiv)

End-to-end semantic role labeling (SRL) has been received increasing interest. It performs the two subtasks of SRL: predicate identification and argument role labeling, jointly. Recent work is mostly focused on graph-based neural models, while the transition-based framework with neural networks which has been widely used in a number of closely-related tasks, has not been studied for the joint task yet. In this paper, we present the first work of transition-based neural models for end-to-end SRL. Our transition model incrementally discovers all sentential predicates as well as their arguments by a set of transition actions. The actions of the two subtasks are executed mutually for full interactions. Besides, we suggest high-order compositions to extract non-local features, which can enhance the proposed transition model further. Experimental results on CoNLL09 and Universal Proposition Bank show that our final model can produce state-of-the-art performance, and meanwhile keeps highly efficient in decoding. We also conduct detailed experimental analysis for a deep understanding of our proposed model.