# Neural Semantic Role Labeling with Dependency Path Embeddings

arXiv: https://arxiv.org/abs/1605.07515 · HTML: https://mlsanity.com/paper/1605.07515/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2016.

## Built on by (most cited first, 15 of 15)

- [Pushing the Limits of ChatGPT on NLP Tasks](https://mlsanity.com/paper/2306.09719/) (2023)
- [End-to-end Semantic Role Labeling with Neural Transition-based Model](https://mlsanity.com/paper/2101.00394/) (2021)
- [Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling](https://mlsanity.com/paper/2112.07874/) (2021)
- [Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling](https://mlsanity.com/paper/2104.07704/) (2021)
- [Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures](https://mlsanity.com/paper/2212.01094/) (2022)
- [An MRC Framework for Semantic Role Labeling](https://mlsanity.com/paper/2109.06660/) (2021)
- [Transition-based Semantic Role Labeling with Pointer Networks](https://mlsanity.com/paper/2205.10023/) (2022)
- [Sister Help: Data Augmentation for Frame-Semantic Role Labeling](https://mlsanity.com/paper/2109.07725/) (2021)
- [Mixture-of-Linguistic-Experts Adapters for Improving and Interpreting Pre-trained Language Models](https://mlsanity.com/paper/2310.16240/) (2023)
- [Revisiting Semantic Role Labeling: Efficient Structured Inference with Dependency-Informed Analysis](https://mlsanity.com/paper/2605.02505/) (2026)
- [Hierarchical Multitask Learning with Dependency Parsing for Japanese Semantic Role Labeling Improves Performance of Argument Identification](https://mlsanity.com/paper/2101.06071/) (2021)
- [Neural Unsupervised Semantic Role Labeling](https://mlsanity.com/paper/2104.09047/) (2021)
- [PriMeSRL-Eval: A Practical Quality Metric for Semantic Role Labeling Systems Evaluation](https://mlsanity.com/paper/2210.06408/) (2022)
- [Semantic Role Labeling of NomBank Partitives](https://mlsanity.com/paper/2412.14328/) (2024)
- [Emergent Semantic Role Understanding in Language Models](https://mlsanity.com/paper/2605.09187/) (2026)

## Abstract (arXiv)

This paper introduces a novel model for semantic role labeling that makes use of neural sequence modeling techniques. Our approach is motivated by the observation that complex syntactic structures and related phenomena, such as nested subordinations and nominal predicates, are not handled well by existing models. Our model treats such instances as sub-sequences of lexicalized dependency paths and learns suitable embedding representations. We experimentally demonstrate that such embeddings can improve results over previous state-of-the-art semantic role labelers, and showcase qualitative improvements obtained by our method.