*SEM 2013 shared task: Semantic Textual Similarity

By Eneko Agirre, Daniel Matthew Cer, Mona T. Diab, Aitor Gonzalez-Agirre, Weiwei Guo. 2013

808 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

SentEval: An Evaluation Toolkit for Universal Sentence Representations (2018, neutral): “We include STS tasks from 2012 (Agirre et al., 2012), 2013 4 (Agirre et al., 2013), 2014 (Agirre et al., 2014), 2015 (Agirre et al., 2015) and 2016 (Agirre et al., 2016).”
Self-training Improves Pre-training for Natural Language Understanding (2020, methodology): “On average, our embeddings outperform previous approaches by 0.2% on STS 2012 to 2016 (Agirre et al., 2012, 2013, 2014, 2015, 2016), and by 0.9% on STS-Benchmark (Cer et al., 2017).”
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019, methodology): “We use the STS tasks 2012 - 2016 (Agirre et al., 2012, 2013, 2014, 2015, 2016), the STS benchmark (Cer et al., 2017), and the SICK-Relatedness dataset (Marelli et al., 2014).”
Continual Learning for Sentence Representations Using Conceptors (2019, methodology): “We evaluated our approach for continual sentence representation learning using semantic textual similarity (STS) datasets (Agirre et al., 2012, 2013, 2014, 2015, 2016).”
SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation (2016, methodology): “The STS shared task has been held annually since 2012, providing a venue for the evaluation of state-of-the-art algorithms and models (Agirre et al., 2012; Agirre et al., 2013; Agirre et al., 2014; Agirre et al., 2015).”
SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021, methodology): “We evaluate on 7 STS tasks: STS 2012–2016 (Agirre et al., 2012, 2013, 2014, 2015, 2016), STS Benchmark (Cer et al., 2017) and SICKRelatedness (Marelli et al., 2014).”
UNAL-NLP: Combining Soft Cardinality Features for Semantic Textual Similarity, Relatedness and Entailment (2014, methodology): “Consequently, the previous evaluation campaigns of this task in SemEval have attracted the attention of many research groups worldwide (Agirre et al., 2012; Agirre et al., 2013).”
Siamese Recurrent Architectures for Learning Sentence Similarity (2016, positive): “Then, our MaLSTM is (pre)trained as previously described on separate sentence-pair data provided for the earlier SemEval 2013 Semantic Textual Similarity task (Agirre and Cer 2013).”
How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings (2019, methodology): “Our input data come from the SemEval Semantic Textual Similarity tasks from years 2012 - 2016 (Agirre et al., 2012, 2013, 2014, 2015).”
All-but-the-Top: Simple and Effective Postprocessing for Word Representations (2017, methodology): “We test the word representations on the 20 textual similarity datasets from the 2012-2015 Se-mEval semantic textual similarity (STS) shared tasks (Agirre et al., 2012, 2013, 2014; Agirrea et al., 2015), and the 2012 SemEval Semantic Related task (SICK) (Marelli et al., 2014).”