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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–225 of 620 papers

TitleStatusHype
Ensemble Technique Utilization for Indonesian Dependency Parser—0
Evaluating automatic cross-domain Dutch semantic role annotation—0
Evaluating distributed word representations for capturing semantics of biomedical concepts—0
Cross-lingual Model Transfer Using Feature Representation Projection—0
Evaluating Inter-Annotator Agreement on Historical Spelling Normalization—0
Event Detection and Co-reference with Minimal Supervision—0
A New Method for Cross-Lingual-based Semantic Role Labeling—0
Expanding VerbNet with Sketch Engine—0
Experiencer-Specific Emotion and Appraisal Prediction—0
Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions?—0
Explaining non-linear Classifier Decisions within Kernel-based Deep Architectures—0
Exploiting Mutual Benefits between Syntax and Semantic Roles using Neural Network—0
Exploiting Zero Pronouns to Improve Chinese Coreference Resolution—0
Exploring Deterministic Constraints: from a Constrained English POS Tagger to an Efficient ILP Solution to Chinese Word Segmentation—0
Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities—0
Extra \~ao de Alvos em Coment\'arios de Not\' em Portugu\^es baseada na Teoria da Centraliza \~ao (Target Extraction in News Reviews in Portuguese based on Centering Theory)—0
AMRize, then Parse! Enhancing AMR Parsing with PseudoAMR Data—0
Extracting Semantic Process Information from the Natural Language in Event Logs—0
Chinese Semantic Role Labeling using High-quality Syntactic Knowledge—0
Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training—0
FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering—0
Crosslingual Induction of Semantic Roles—0
Fast and Accurate Span-based Semantic Role Labeling as Graph Parsing—0
Feature-based Neural Language Model and Chinese Word Segmentation—0
Cross-lingual Discourse Relation Analysis: A corpus study and a semi-supervised classification system—0
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All datasetsOntoNotesCoNLL 2005CoNLL05 BrownCoNLL05 WSJCoNLL12CoNLL-2009CoNLL-2012

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HeSyFuF188.59—Unverified
2CRF2o + RoBERTaF188.32—Unverified
3MRC-SRLF188.3—Unverified
4ReCAT(pretrained on wikitext103)F188—Unverified
5SRL-MM + XLNetF187.67—Unverified
6CRF2o + BERTF187.66—Unverified
7RoBERTa+RegCCRFF187.51—Unverified
8RoBERTa+CRFF187.27—Unverified
9BiLSTM-Span (Ensemble)F187—Unverified
10BiLSTM-SpanF186.2—Unverified
#ModelMetricClaimedVerifiedStatus
1MRC-SRLF190—Unverified
2SRL-MM + XLNetF189.8—Unverified
3CRF2o + RoBERTaF189.54—Unverified
4HeSyFuF189.04—Unverified
5CRF2o + BERTF189.03—Unverified
6Mohammadshahi and Henderson (2021)F188.93—Unverified
7BiLSTM-Span (Ensemble, predicates given)F188.5—Unverified
8CRF2oF187.87—Unverified
9Li et al. (2019) (Ensemble)F187.7—Unverified
10BiLSTM-SpanF187.6—Unverified
#ModelMetricClaimedVerifiedStatus
1DeepStruct multi-task w/ finetuneF192.1—Unverified
2DeepStruct multi-taskF192—Unverified
#ModelMetricClaimedVerifiedStatus
1DeepStruct multi-taskF195.5—Unverified
2DeepStruct multi-task w/ finetuneF195.2—Unverified
#ModelMetricClaimedVerifiedStatus
1DeepStruct multi-taskF197.2—Unverified
2DeepStruct multi-task w/ finetuneF196—Unverified
#ModelMetricClaimedVerifiedStatus
1Ours (High-Order model)F1 (Arg.)90.2—Unverified
#ModelMetricClaimedVerifiedStatus
1HeSyFuAvg. F188.59—Unverified