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.

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Challenge: Using propBank-style semantic role labeling, we reduce the task to syntactic dependency parsing.
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Towards Standardized Annotation and Parsing for Korean FrameNet (2024.lrec-main)

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Challenge: Existing studies on Korean FrameNet have focused on English, but annotations are not optimally designed for Korean.
Approach: They propose a morphologically enhanced annotation strategy for Korean FrameNet datasets and parsing by leveraging the CoNLL-U format.
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Label Definitions Improve Semantic Role Labeling (2022.naacl-main)

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Challenge: Existing work on semantic role labeling treats symbolic labels as symbolic . labeled data is costly and often lacking in many tasks, domains, and languages.
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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 .
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Constructing Web-Accessible Semantic Role Labels and Frames for Japanese as Additions to the NPCMJ Parsed Corpus (2020.lrec-1)

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Challenge: Adding semantic role labels to the NPCMJ will help language learners and linguists search for syntactic and semantic features.
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Translate and Label! An Encoder-Decoder Approach for Cross-lingual Semantic Role Labeling (D19-1)

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Challenge: Unlike annotation projection techniques, our model does not need parallel data during inference time.
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Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach (2020.coling-main)

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Challenge: Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling.
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A Flexible and Easy-to-use Semantic Role Labeling Framework for Different Languages (C18-2)

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Challenge: DAMESRL is an open source framework for deep semantic role labeling . language-specific characteristics and the available amount of training data influence the optimal model structure .
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Alignment-free Cross-lingual Semantic Role Labeling (2020.emnlp-main)

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Challenge: Existing approaches to semantic role labeling rely on word alignments, translation engines or preprocessing tools.
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Transformer-based Swedish Semantic Role Labeling through Transfer Learning (2024.lrec-main)

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Challenge: Semantic Role Labeling (SRL) is a task in natural language understanding where the goal is to extract semantic roles for a given sentence.
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