The PASCAL Recognising Textual Entailment Challenge

By Ido Dagan, Oren Glickman, Bernardo Magnini. 2005

2,682 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Long Short-Term Memory-Networks for Machine Reading (2016, background): “…et al., 2011), knowledge base population and construction (Ji and Grishman, 2011; Niu et al., 2012; Carlson et al., 2010), ontology learning (Poon and Domingos, 2010b), textual entailment (Dagan et al., 2005), and the creation of proposition stores (Pe˜nas and Hovy, 2010; Schubert and Tong, 2003).”
SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference (2018, background): “…dominant entailment paradigm asks if two natural language sentences (the ‘premise’ and the ‘hypothesis’) describe the same set of possible worlds (Dagan et al., 2006; Bowman et al., 2015), here we focus on whether a (multiple-choice) ending describes a possible ( future ) world that can be…”
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding (2018, methodology): “We combine the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al.”
A large annotated corpus for learning natural language inference (2015, background): “Thus, natural language inference (NLI) — characterizing and using these relations in computational systems (Fyodorov et al., 2000; Condoravdi et al., 2003; Bos and Markert, 2005; Dagan et al., 2006; MacCartney and Manning, 2009) — is essential in tasks ranging from information retrieval to semantic ”
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (2019, background): “All data was sourced from TensorFlow Datasets.9 GLUE [Wang et al., 2018] and SuperGLUE [Wang et al., 2019b] each comprise a collection of text classification tasks meant to test general language understanding abilities: • Sentence acceptability judgment (CoLA [Warstadt et al., 2018]) • Sentiment ana”
RoBERTa: A Robustly Optimized BERT Pretraining Approach (2019, background): “girre et al., 2007), Quora Question Pairs (QQP) (Iyer et al., 2016), MultiGenre NLI (MNLI) (Williams et al., 2018), Question NLI (QNLI) (Rajpurkar et al., 2016), Recognizing Textual Entailment (RTE) (Dagan et al. 2006; Bar-Haim et al. 2006; Giampiccolo et al., 2007; Bentivogli et al., 2009) and Wino”
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension (2019, methodology): “Table 2 compares the performance of BART with several recent approaches on the well-studied SQuAD and GLUE tasks (Warstadt et al., 2018; Socher et al., 2013; Dolan & Brockett, 2005; Agirre et al., 2007; Williams et al., 2018; Dagan et al., 2006; Levesque et al., 2011).”
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations (2019, background): “…(QQP; Iyer et al., 2017), Multi-Genre NLI (MNLI; Williams et al., 2018), Question NLI (QNLI; Rajpurkar et al., 2016), Recognizing Textual Entailment (RTE; Dagan et al., 2005; Bar-Haim et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., 2009) and Winograd NLI (WNLI; Levesque et al., 2012).”
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems (2019, methodology): “RTE was previously included in GLUE, and we use the same data and format as before: We merge data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009).9 All datasets are combined and converted to two-class classification: e”
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference (2019, background): “s this issue in the domain of natural language inference (NLI), the task of determining whether a premise sentence entails (i.e., implies the truth of) a hypothesis sentence (Condoravdi et al., 2003; Dagan et al., 2006; Bowman et al., 2015). As in other domains, neural NLI models have been shown to ”
Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks (2018, methodology): “al., 2013); semantic similarity with the MSR Paraphrase Corpus (MRPC; Dolan and Brockett, 2005) and STSBenchmark (STS;Ceretal.,2017); andtextual entailment with a subset of the RTE challenge corpora (Dagan et al., 2006, et seq.), and data from SQuAD (QNLI, Rajpurkar et al., 2016) and the Winograd Sc”
The Natural Language Decathlon: Multitask Learning as Question Answering (2018, background): “Natural Language Inference NLI has a long history playing roles in tasks like information retrieval and semantic parsing [Fyodorov et al., 2000, Condoravdi et al., 2003, Bos and Markert, 2005, Dagan et al., 2005, MacCartney and Manning, 2009].”
Annotation Artifacts in Natural Language Inference Data (2018, background): “Natural language inference (NLI; also known as recognizing textual entailment, or RTE) is a widely-studied task in natural language processing, to which many complex semantic tasks, such as question answering and text summarization, can be reduced (Dagan et al., 2006).”
SpanBERT: Improving Pre-training by Representing and Predicting Spans (2019, background): “, 2016), RTE (Dagan et al., 2005; Bar-Haim et al., 2006; Giampiccolo et al., 2007), and WNLI (Levesque et al.”
Unified Language Model Pre-training for Natural Language Understanding and Generation (2019, methodology): “ge understanding tasks, including question answering [33], linguistic acceptability [46], sentiment analysis [38], text similarity [5], paraphrase detection [10], and natural language inference (NLI) [7, 2, 17, 3, 24, 47]. Our model is ﬁne-tuned as a bidirectional LM. We use Adamax [21] as our optim”