InVeRo: Making Semantic Role Labeling Accessible with Intelligible Verbs and Roles (2020.emnlp-demos)
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| Challenge: | Semantic Role Labeling (SRL) is dependent on complex linguistic resources and sophisticated neural models, which makes the task difficult to approach for non-experts. |
| Approach: | They propose a platform for semantic role labeling that provides verb sense and semantic role information with an easy to use Web interface and RESTful APIs. |
| Outcome: | The proposed system provides human-readable verb sense and semantic role information with an easy to use Web interface and RESTful APIs. |
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InVeRo-XL: Making Cross-Lingual Semantic Role Labeling Accessible with Intelligible Verbs and Roles (2021.emnlp-demo)
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| Challenge: | InVeRo-XL is an off-the-shelf system capable of annotating text with predicate sense and semantic role labels from 7 predicated-argument structure inventories in more than 40 languages. |
| Approach: | They propose to use RESTful API and Web interface to integrate sentence-level semantics into cross-lingual downstream tasks. |
| Outcome: | The proposed system can annotate text with predicate sense and semantic role labels from 7 predicated-argument structure inventories in more than 40 languages. |
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. |
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Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities (2023.findings-acl)
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| Challenge: | Existing systems for SRL are incapable of transferring knowledge across different predicate types. |
| Approach: | They propose a new PropBank dataset which boasts wide coverage of multiple predicate types and a manually-annotated challenge set which gives equal importance to verbal, nominal, and adjectival predicates. |
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How to Best Use Syntax in Semantic Role Labelling (P19-1)
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| 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. |
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A Full End-to-End Semantic Role Labeler, Syntactic-agnostic Over Syntactic-aware? (C18-1)
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| Challenge: | Existing models for semantic role labeling are syntax-agnostic, but outperform them on benchmarks. |
| Approach: | They propose an end-to-end neural model which tackles the SRL problem in one shot . they augment the encoder with a non-linear transformation to distinguish the predicate and the argument . |
| Outcome: | The proposed model outperforms state-of-the-art syntax-aware SRL systems on CoNLL-2008 and 2009 benchmarks for English and Chinese. |
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 . |
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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. |
Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures (2022.findings-emnlp)
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| Challenge: | Existing approaches to Semantic Role Labeling rely on discrete labels to classify predicate senses and their arguments. |
| Approach: | They propose a generalized formulation of Semantic Role Labeling that leverages Definition Modeling to describe predicate-argument structures using natural language definitions instead of discrete labels. |
| Outcome: | The proposed model can describe predicate-argument structures using natural language definitions instead of discrete labels. |
A Span Selection Model for Semantic Role Labeling (D18-1)
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| Challenge: | Existing models for semantic role labeling use BIO tags to predict argument spans . but performance of these approaches is weak . |
| Approach: | They propose a span-based model that takes into account all possible argument spans and scores them for each label. |
| Outcome: | The proposed model achieves state-of-the-art results on the CoNLL-2005 and 2012 datasets. |