| Challenge: | Existing studies on integrating external information into NLP tasks focus on word-level shallow features such as POS or chunk tags. |
| Approach: | They propose to integrate syntactic information into a neural ELMo-based SRL sequence labelling model by using a constituency representation as input features. |
| Outcome: | The proposed approach improves performance on the in-domain CoNLL’05 and CoNll’12 benchmarks. |
Similar Papers
Syntax in End-to-End Natural Language Processing (2021.emnlp-tutorials)
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| Challenge: | tutorial focuses on syntactic parsing and syntax in end-to-end natural language processing (NLP) tasks. |
| Approach: | tutorial will introduce syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks. |
| Outcome: | This tutorial will introduce the background and the latest progress of syntactic parsing and SRL/NMT. |
A Unified Syntax-aware Framework for Semantic Role Labeling (D18-1)
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| Challenge: | Syntactic information has been paid a great attention over the role of enhancing SRL . but the gap between syntax-aware and syntax-gnostic SRL is smaller . a new framework proposes syntax-based SRL for a wide range of NLP tasks . |
| Approach: | They propose to extend existing models to investigate more effective ways of incorporating syntax into sequential neural networks. |
| Outcome: | The proposed framework outperforms existing models on CoNLL-2009 benchmarks in English and Chinese. |
Syntax-driven Approach for Semantic Role Labeling (2022.lrec-1)
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| Challenge: | Existing studies focus on auto-generated syntactic knowledge to enhance semantic role labeling . experimental results show that map memories can enhance SRL . |
| Approach: | They propose to map memories to enhance semantic role labeling by encoding auto-generated syntactic knowledge from off-the-shelf toolkits. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art results on two English benchmark datasets. |
Semantic Role Labeling as Syntactic Dependency Parsing (2020.emnlp-main)
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| Challenge: | Using propBank-style semantic role labeling, we reduce the task to syntactic dependency parsing. |
| Approach: | They propose to convert SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format. |
| Outcome: | The proposed scheme reduces the task of (span-based) PropBank-style semantic role labeling to syntactic dependency parsing. |
Syntax-Enhanced Self-Attention-Based Semantic Role Labeling (D19-1)
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| Challenge: | Abstract: Syntax is the bridge to semantics, but recent studies have discussed the necessity of syntax in the context of SRL. |
| Approach: | They propose a syntax-enhanced self-attention model that incorporates syntactic knowledge into the SRL task effectively. |
| Outcome: | The proposed model achieves state-of-the-art for the Chinese SRL task on the CoNLL-2009 dataset. |
LLMs Can Also Do Well! Breaking Barriers in Semantic Role Labeling via Large Language Models (2025.findings-acl)
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| Challenge: | Semantic role labeling (SRL) is a crucial task of natural language processing (NLP). |
| Approach: | They propose to equip LLMs with retrieval-augmented generation and self-correction mechanisms to enable SRL to perform better in Chinese and English. |
| Outcome: | The proposed method achieves state-of-the-art in Chinese and English on three widely-used benchmarks. |
Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data (D18-1)
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| Challenge: | a learner language (interlanguage) is an idiolect developed by a learning of a second or foreign language. |
| Approach: | They propose to use semantic role labeling as a case task to parse interlanguages . they then evaluate three off-the-shelf SRL systems to gauge how successful they are . |
| Outcome: | The proposed model achieves an F-score of 72.06, a 2.02 point improvement over the baseline. |
Using Semantic Role Labeling to Improve Neural Machine Translation (2022.lrec-1)
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| Challenge: | despite progress in machine translation, some form of language understanding may be desirable . current systems rely on pattern recognition, but some form may be useful . |
| Approach: | They use semantic role labeling to annotate a standard parallel corpus with semantic roles . they then train a neural machine translation system using the annotated corpus and original unannotated text . |
| Outcome: | The proposed system improves BLEU scores for English, French, German, Greek and Spanish. |
Syntax-aware Neural Semantic Role Labeling with Supertags (N19-1)
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| Challenge: | a new syntax-aware model for dependency-based semantic role labeling outperforms syntax-based models for English and Spanish. |
| Approach: | They propose a syntax-aware model for dependency-based semantic role labeling that outperforms syntax-based models for English and Spanish. |
| Outcome: | The proposed model outperforms syntax-agnostic models for English and Spanish. |
Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling (2021.findings-acl)
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| Challenge: | Existing studies use only one singleton syntax for semantic role labeling (SRL). |
| Approach: | They propose a TreeLSTM-based integration that integrates phrasal boundaries and semantic relations from dependency into a labelaware GCN solution for simultaneously modeling syntactic edges and labels. |
| Outcome: | The proposed system achieves state-of-the-art performance on span-based and dependency-based SRL. |