Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks (2020.acl-main)
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| Challenge: | Opinion role labeling (ORL) is a fine-grained opinion analysis task . due to the scarcity of labeled data, ORL remains challenging for data-driven methods due to its complexity and complexity. |
| Approach: | They propose to integrate syntactic knowledge into ORL models by comparing and integrating different representations and using dependency graph convolutional networks to encode parser information at different processing levels. |
| Outcome: | The proposed model achieves 4.34 higher F1 score than the current state-of-the-art. |
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Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling (N19-1)
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| Challenge: | Existing work on opinion role labeling (ORL) is highly correlative with semantic role labeled (SRL) SRL is used to identify opinion holders and holder expressions for a given predicate. |
| Approach: | They propose a method to enhance opinion role labeling by presenting semantic-aware word representations which are learned from SRL. |
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SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning with Semantic Role Labeling (N18-1)
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| Challenge: | Recent neural approaches do not outperform the state-of-the-art feature-based models for Opinion Role Labeling (ORL). |
| Approach: | They propose to use multi-task learning to improve Opinion Role Labeling by using a related task which has substantially more data. |
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Chinese Opinion Role Labeling with Corpus Translation: A Pivot Study (2021.emnlp-main)
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| Challenge: | Unlike most of the previous work focusing on the English language, this paper focuses on the Chinese ORL task. |
| Approach: | They propose to use a standard English MPQA dataset to construct a Chinese ORL dataset and investigate the effectiveness of cross-lingual transfer methods. |
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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. |
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Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis (2021.acl-long)
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| Challenge: | Existing methods to model relationships between aspects and opinion words are inefficient due to informal expressions and complexity of online reviews. |
| Approach: | They propose a dual graph convolutional networks model that considers complementarity of syntax structures and semantic correlations simultaneously. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public datasets and validates it. |
Linguistically-Informed Self-Attention for Semantic Role Labeling (D18-1)
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| Challenge: | Existing models of semantic role labeling use no explicit linguistic features. prior work has shown that syntax trees can dramatically improve SRL decoding. |
| Approach: | They propose a neural network model that incorporates syntax using only raw tokens . they show that LISA out-performs the state-of-the-art with contextually-encoded word representations a 1.0 F1 on newswire and 2.0 F1 in out-of domain text . |
| Outcome: | The proposed model outperforms the state-of-the-art model with word embeddings and predicted predicates. |
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. |
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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. |
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). |
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. |