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 301–350 of 620 papers

TitleStatusHype
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
Fast and Accurate Span-based Semantic Role Labeling as Graph Parsing—0
Feature-based Neural Language Model and Chinese Word Segmentation—0
Feature Generation for Robust Semantic Role Labeling—0
Filling Conversation Ellipsis for Better Social Dialog Understanding—0
Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes—0
Find the word that does not belong: A Framework for an Intrinsic Evaluation of Word Vector Representations—0
Fine-tuning and Sampling Strategies for Multimodal Role Labeling of Entities under Class Imbalance—0
First approach toward Semantic Role Labeling for Basque—0
First steps towards a Predicate Matrix—0
Focusing Annotation for Semantic Role Labeling—0
Forest Reranking through Subtree Ranking—0
FrameFOR -- Uma Base de Conhecimento de Frames Sem\^anticos para Per\' de Inform\'atica (FrameFOR - a Knowledge Base of Semantic Frames for Digital Forensics)[In Portuguese]—0
FrameIt: Ontology Discovery for Noisy User-Generated Text—0
FrameNet on the Way to Babel: Creating a Bilingual FrameNet Using Wiktionary as Interlingual Connection—0
Frame-Semantic Role Labeling with Heterogeneous Annotations—0
Frame Semantics across Languages: Towards a Multilingual FrameNet—0
Framework of Semantic Role Assignment based on Extended Lexical Conceptual Structure: Comparison with VerbNet and FrameNet—0
FRASE: Structured Representations for Generalizable SPARQL Query Generation—0
Friend-training: Learning from Models of Different but Related Tasks—0
From Natural Language Specifications to Program Input Parsers—0
From Textual Information Sources to Linked Data in the Agatha Project—0
Generating Adequate Distractors for Multiple-Choice Questions—0
Generating High Quality Proposition Banks for Multilingual Semantic Role Labeling—0
Generating Training Data for Semantic Role Labeling based on Label Transfer from Linked Lexical Resources—0
German and French Neural Supertagging Experiments for LTAG Parsing—0
Getting the Roles Right: Using FrameNet in NLP—0
Going beyond sentences when applying tree kernels—0
Good Automatic Authentication Question Generation—0
Gradient-based Inference for Networks with Output Constraints—0
Graph Convolutional Network with Sequential Attention For Goal-Oriented Dialogue Systems—0
Graph Methods for Multilingual FrameNets—0
Grounded Semantic Role Labeling—0
Grounding Semantic Roles in Images—0
Handling Ambiguities of Bilingual Predicate-Argument Structures for Statistical Machine Translation—0
Hierarchical Multitask Learning with Dependency Parsing for Japanese Semantic Role Labeling Improves Performance of Argument Identification—0
Hierarchical Recurrent Neural Network for Document Modeling—0
High-Order Low-Rank Tensors for Semantic Role Labeling—0
High-order Refining for End-to-end Chinese Semantic Role Labeling—0
High Performance Word Sense Alignment by Joint Modeling of Sense Distance and Gloss Similarity—0
How can NLP Tasks Mutually Benefit Sentiment Analysis? A Holistic Approach to Sentiment Analysis—0
HYENA: Hierarchical Type Classification for Entity Names—0
HYENA-live: Fine-Grained Online Entity Type Classification from Natural-language Text—0
ICL-HD at SemEval-2016 Task 8: Meaning Representation Parsing - Augmenting AMR Parsing with a Preposition Semantic Role Labeling Neural Network—0
Identifying economic narratives in large text corpora -- An integrated approach using Large Language Models—0
Identifying Key Concepts from EHR Notes Using Domain Adaptation—0
Identifying Pronominal Verbs: Towards Automatic Disambiguation of the Clitic `se' in Portuguese—0
Explicit Contextual Semantics for Text Comprehension—0
Image Annotation with ISO-Space: Distinguishing Content from Structure—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