Papers with Semantic role labeling
A Linguistically-Informed Annotation Strategy for Korean Semantic Role Labeling (2024.lrec-main)
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| Challenge: | Semantic role labeling is an essential component of semantic and syntactic processing of natural languages. |
| Approach: | They propose an annotation strategy for Korean semantic role labeling that is in line with the previously proposed linguistic theories as well as the distinct properties of the Korean language. |
| Outcome: | The proposed annotation strategy is consistent with the proposed linguistic theories and the distinct properties of the Korean language. |
High-order Semantic Role Labeling (2020.findings-emnlp)
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| Challenge: | Experimental results show that high-order structural learning techniques are beneficial to SRL models . high-level features and structure learning are not common in deep neural networks . |
| Approach: | They propose a high-order graph structure for a neural semantic role labeling model . it explicitly considers the isolated predicate-argument pairs and interaction between them . |
| Outcome: | The proposed model can explicitly consider the isolated predicate-argument pairs and the interaction between the predicates-argoments pairs. |
PriMeSRL-Eval: A Practical Quality Metric for Semantic Role Labeling Systems Evaluation (2023.findings-eacl)
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Ishan Jindal, Alexandre Rademaker, Khoi-Nguyen Tran, Huaiyu Zhu, Hiroshi Kanayama, Marina Danilevsky, Yunyao Li
| Challenge: | Existing evaluation scripts for semantic role labeling do not consider error propagation . existing evaluation script does not consider argument independent of predicate sense . |
| Approach: | They propose a more strict SRL evaluation metric PriMeSRL to address these issues . they propose to use a metric that measures the quality of the underlying SRL models . |
| Outcome: | The proposed metric reduces quality evaluation of all SoTA SRL models and penalizes failures. |
Universal Proposition Bank 2.0 (2022.lrec-1)
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Ishan Jindal, Alexandre Rademaker, Michał Ulewicz, Ha Linh, Huyen Nguyen, Khoi-Nguyen Tran, Huaiyu Zhu, Yunyao Li
| Challenge: | Semantic role labeling (SRL) is a shallow semantic parsing task that identifies "who did what to whom when, where etc." SRL is useful in a wide range of downstream NLP tasks and real-world applications. |
| Approach: | They propose a method to generate shallow semantic parsing tasks using monolingual SRL and multilingual parallel data. |
| Outcome: | The proposed method improves the quality of the generated propbanks. |
ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs (2022.findings-naacl)
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| Challenge: | Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations. |
| Approach: | They propose to use auxiliary tasks which are semantically or formally related to enhance AMR parsing. |
| Outcome: | The proposed method achieves state-of-the-art performance on benchmarks especially in topology-related scores. |
Syntax for Semantic Role Labeling, To Be, Or Not To Be (P18-1)
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| Challenge: | Existing neural SRL models lack syntactic backbone for performance, limiting its use in deep learning. |
| Approach: | They propose an enhanced argument labeling model with extended korder argument pruning algorithm for effectively exploiting syntactic information. |
| Outcome: | The proposed model achieves state-of-the-art on the CoNLL-2008 and 2009 benchmarks in English and Chinese. |
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. |
Gold Standard Annotations for Preposition and Verb Sense with Semantic Role Labels in Adult-Child Interactions (C18-1)
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| Challenge: | Existing corpus of child-directed speech augments existing corpus for semantic role labels . sense and number of arguments were open to multiple interpretations due to rapidly changing discourse . |
| Approach: | They propose to augment an existing corpus of child-directed speech to provide supervised learning of semantic role labels. |
| Outcome: | The resulting corpus is a gold standard for supervised learning of semantic role labels in child-directed speech. |
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. |
CLAR: A Cross-Lingual Argument Regularizer for Semantic Role Labeling (2020.findings-emnlp)
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| Challenge: | Existing methods for training one model on multiple languages outperform monolingual baselines for low resource languages. |
| Approach: | They propose a method to combine training data from multiple languages to create a shared representation space for the model. |
| Outcome: | The proposed method outperforms monolingual and polyglot training on low resource languages. |
Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling (2020.emnlp-main)
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| Challenge: | Semantic role labeling (SRL) is the task of identifying predicates and labeling argument spans with semantic roles. |
| Approach: | They propose to use graph convolutional networks to encode constituents and inform an SRL system by combining word representations of the first and last words in a constituent tree. |
| Outcome: | The proposed model is compared with other models and shows that it is more efficient than dependency trees. |
Semantic Role Labeling with Associated Memory Network (N19-1)
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| Challenge: | Existing work on semantic role labeling has been focused on using deep learning methods to solve the task. |
| Approach: | They propose a syntax-agnostic SRL model enhanced by the proposed associated memory network which makes use of inter-sentence attention of label-known associated sentences as a kind of memory to further enhance dependency-based SRL. |
| Outcome: | The proposed model achieves state-of-the-art on CoNLL-2009 benchmark datasets showing that it is not dependent on external resources. |
Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures inside Arguments (2022.coling-1)
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| Challenge: | Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based. |
| Approach: | They propose to regard flat argument spans as latent subtrees, thus reducing SRL to a tree parsing task. |
| Outcome: | The proposed model performs better than previous syntax-agnostic models on CoNLL05 and CoNll12 benchmarks. |
A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling (D19-1)
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| Challenge: | Semantic role labeling (SRL) aims to identify the predicate-argument structure of a sentence. |
| Approach: | They propose to use a unified span-based model for Chinese SRL as a strong baseline. |
| Outcome: | The proposed framework achieves state-of-the-art 87.54 and 88.5 F1 scores on the Chinese Proposition Bank and CoNLL-2009 datasets. |
Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks (D19-1)
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| Challenge: | State-of-the-art SRL models do not model non-local interaction between arguments . e.g., LSTMs do not allow for efficient inference . |
| Approach: | They propose a new approach to model interactions between arguments using capsule networks . they analyze errors in the refinement procedure by capturing intuition in a flexible way . |
| Outcome: | The proposed model outperforms the baseline model on all 7 languages and achieves state-of-the-art results on 5 languages including English. |
Learning Semantic Role Labeling from Compatible Label Sequences (2023.findings-emnlp)
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| Challenge: | Prior work has shown that cross-task interaction helps, but only explored multitask learning so far. |
| Approach: | They propose a framework that jointly models VerbNet and PropBank labels as one sequence and enforcing Semlink constraints during decoding improves the overall F1 . |
| Outcome: | The proposed model outperforms the prior best in-domain model by 3.5 (VerbNet) and 0.8 (PropBank). |
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. |