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 151–200 of 620 papers

TitleStatusHype
Comparing Span Extraction Methods for Semantic Role Labeling—0
A Semantic Role-based Approach to Open-Domain Automatic Question Generation—0
Comparing Czech and English AMRs—0
A Semantic Approach to Summarization—0
An Analysis under a Unified Fomulation of Learning Algorithms with Output Constraints—0
Combining Seemingly Incompatible Corpora for Implicit Semantic Role Labeling—0
A Robust Approach to Aligning Heterogeneous Lexical Resources—0
CMILLS: Adapting Semantic Role Labeling Features to Dependency Parsing—0
CLAR: A Cross-Lingual Argument Regularizer for Semantic Role Labeling—0
Argument Linking: A Survey and Forecast—0
AMR Parsing with Instruction Fine-tuned Pre-trained Language Models—0
Advancements in Reordering Models for Statistical Machine Translation—0
Adapting a part-of-speech tagset to non-standard text: The case of STTS—0
Chinese Semantic Role Labeling with Bidirectional Recurrent Neural Networks—0
Chinese Semantic Role Labeling using High-quality Syntactic Knowledge—0
Argument Inference from Relevant Event Mentions in Chinese Argument Extraction—0
Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?—0
AMRize, then Parse! Enhancing AMR Parsing with PseudoAMR Data—0
Capturing Argument Relationship for Chinese Semantic Role Labeling—0
Can Selectional Preferences Help Automatic Semantic Role Labeling?—0
Can predicate-argument relationships be extracted from UD trees?—0
A Progressive Learning Approach to Chinese SRL Using Heterogeneous Data—0
A Dual-Layer Semantic Role Labeling System—0
Can Discourse Relations be Identified Incrementally?—0
Building Japanese Predicate-argument Structure Corpus using Lexical Conceptual Structure—0
Applying Collocation Segmentation to the ACL Anthology Reference Corpus—0
A Pilot PropBank Annotation for Quranic Arabic—0
Alignment-free Cross-lingual Semantic Role Labeling—0
Bootstrapping Semantic Role Labelers from Parallel Data—0
Book Reviews: Ontology-Based Interpretation of Natural Language by Philipp Cimiano, Christina Unger and John McCrae—0
Any-language frame-semantic parsing—0
Encoding of Compounds in Swedish FrameNet—0
Empty Argument Insertion in the Hindi PropBank—0
Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations—0
An Unsupervised Model for Instance Level Subcategorization Acquisition—0
Aligning Opinions: Cross-Lingual Opinion Mining with Dependencies—0
A Distant Supervision Approach to Semantic Role Labeling—0
Active Data Sampling and Generation for Bias Remediation—0
Efficient Inference and Structured Learning for Semantic Role Labeling—0
Bilingual English-Czech Valency Lexicon Linked to a Parallel Corpus—0
Effectiveness and Efficiency of Open Relation Extraction—0
Beyond The Text: Analysis of Privacy Statements through Syntactic and Semantic Role Labeling—0
Dynamic web service deployment in a cloud environment—0
Duluth: Word Sense Discrimination in the Service of Lexicography—0
Encoding Syntactic Constituency Paths for Frame-Semantic Parsing with Graph Convolutional Networks—0
End-to-End Learning for Structured Prediction Energy Networks—0
End-to-end learning of semantic role labeling using recurrent neural networks—0
Better Word Representations with Recursive Neural Networks for Morphology—0
Domain Specific Automatic Question Generation from Text—0
Annotating the Little Prince with Chinese AMRs—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