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 601–620 of 620 papers

TitleStatusHype
Comparing Span Extraction Methods for Semantic Role Labeling—0
Compositional and Lexical Semantics in RoBERTa, BERT and DistilBERT: A Case Study on CoQA—0
Computational Frameworks for Supporting Textual Inference—0
Computational linking theory—0
Computing Word Classes Using Spectral Clustering—0
Concept-based Selectional Preferences and Distributional Representations from Wikipedia Articles—0
The Semantic Proto-Role Linking Model—0
Constructing Holistic Spatio-Temporal Scene Graph for Video Semantic Role Labeling—0
context2vec: Learning Generic Context Embedding with Bidirectional LSTM—0
The Treebanked Conspiracy. Actors and Actions in Bellum Catilinae—0
Context or No Context? A preliminary exploration of human-in-the-loop approach for Incremental Temporal Summarization in meetings—0
Conversational Semantic Role Labeling—0
Conversational Semantic Role Labeling with Predicate-Oriented Latent Graph—0
Copenhagen-Malm\"o: Tree Approximations of Semantic Parsing Problems—0
Corpora Annotated with Negation: An Overview—0
Corpus-Driven Thematic Hierarchy Induction—0
``Could you make me a favour and do coffee, please?'': Implications for Automatic Error Correction in English and Dutch—0
To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP—0
Cross-Document Non-Fiction Narrative Alignment—0
Cross-lingual alignment transfer: a chicken-and-egg story?—0
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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