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. |
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| Challenge: | Using propBank-style semantic role labeling, we reduce the task to syntactic dependency parsing. |
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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. |
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| Challenge: | Semantic role labeling (SRL) is a crucial task of natural language processing (NLP). |
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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 . |
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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. |
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On the Benefit of Syntactic Supervision for Cross-lingual Transfer in Semantic Role Labeling (2021.emnlp-main)
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| Challenge: | Recent advances in neural architectures and pre-trained representations have greatly improved the performance of fully-supervised semantic role labeling (SRL) but there are limitations in the availability of supervised training data. |
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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 . |
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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. |
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