SOTAVerified

Semantic Role Labeling

Semantic role labeling aims to model the predicate-argument structure of a sentence and is often described as answering "Who did what to whom". BIO notation is typically used for semantic role labeling.

Example:

| Housing | starts | are | expected | to | quicken | a | bit | from | August’s | pace | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | B-ARG1 | I-ARG1 | O | O | O | V | B-ARG2 | I-ARG2 | B-ARG3 | I-ARG3 | I-ARG3 |

Papers

Showing 176200 of 620 papers

TitleStatusHype
Applying Collocation Segmentation to the ACL Anthology Reference Corpus0
A Pilot PropBank Annotation for Quranic Arabic0
Alignment-free Cross-lingual Semantic Role Labeling0
Bootstrapping Semantic Role Labelers from Parallel Data0
Book Reviews: Ontology-Based Interpretation of Natural Language by Philipp Cimiano, Christina Unger and John McCrae0
Any-language frame-semantic parsing0
Encoding of Compounds in Swedish FrameNet0
Empty Argument Insertion in the Hindi PropBank0
Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations0
An Unsupervised Model for Instance Level Subcategorization Acquisition0
Aligning Opinions: Cross-Lingual Opinion Mining with Dependencies0
A Distant Supervision Approach to Semantic Role Labeling0
Active Data Sampling and Generation for Bias Remediation0
Efficient Inference and Structured Learning for Semantic Role Labeling0
Bilingual English-Czech Valency Lexicon Linked to a Parallel Corpus0
Effectiveness and Efficiency of Open Relation Extraction0
Beyond The Text: Analysis of Privacy Statements through Syntactic and Semantic Role Labeling0
Dynamic web service deployment in a cloud environment0
Duluth: Word Sense Discrimination in the Service of Lexicography0
Encoding Syntactic Constituency Paths for Frame-Semantic Parsing with Graph Convolutional Networks0
End-to-End Learning for Structured Prediction Energy Networks0
End-to-end learning of semantic role labeling using recurrent neural networks0
Better Word Representations with Recursive Neural Networks for Morphology0
Domain Specific Automatic Question Generation from Text0
Annotating the Little Prince with Chinese AMRs0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HeSyFuF188.59Unverified
2CRF2o + RoBERTaF188.32Unverified
3MRC-SRLF188.3Unverified
4ReCAT(pretrained on wikitext103)F188Unverified
5SRL-MM + XLNetF187.67Unverified
6CRF2o + BERTF187.66Unverified
7RoBERTa+RegCCRFF187.51Unverified
8RoBERTa+CRFF187.27Unverified
9BiLSTM-Span (Ensemble)F187Unverified
10BiLSTM-SpanF186.2Unverified
#ModelMetricClaimedVerifiedStatus
1MRC-SRLF190Unverified
2SRL-MM + XLNetF189.8Unverified
3CRF2o + RoBERTaF189.54Unverified
4HeSyFuF189.04Unverified
5CRF2o + BERTF189.03Unverified
6Mohammadshahi and Henderson (2021)F188.93Unverified
7BiLSTM-Span (Ensemble, predicates given)F188.5Unverified
8CRF2oF187.87Unverified
9Li et al. (2019) (Ensemble)F187.7Unverified
10BiLSTM-SpanF187.6Unverified
#ModelMetricClaimedVerifiedStatus
1DeepStruct multi-task w/ finetuneF192.1Unverified
2DeepStruct multi-taskF192Unverified
#ModelMetricClaimedVerifiedStatus
1DeepStruct multi-taskF195.5Unverified
2DeepStruct multi-task w/ finetuneF195.2Unverified
#ModelMetricClaimedVerifiedStatus
1DeepStruct multi-taskF197.2Unverified
2DeepStruct multi-task w/ finetuneF196Unverified
#ModelMetricClaimedVerifiedStatus
1Ours (High-Order model)F1 (Arg.)90.2Unverified
#ModelMetricClaimedVerifiedStatus
1HeSyFuAvg. F188.59Unverified