The Seventh PASCAL Recognizing Textual Entailment Challenge

By Luisa Bentivogli, Peter Clark, Ido Dagan, Danilo Giampiccolo. 2011

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Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding (2018, methodology): “ognizing Textual Entailment (RTE) datasets come from a series of annual challenges for the task of textual entailment, also known as NLI. We combine the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009)5. Each examp”
RoBERTa: A Robustly Optimized BERT Pretraining Approach (2019, background): “, 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 Winograd NLI (WNLI) (Le”
Adversarial NLI: A New Benchmark for Natural Language Understanding (2019, methodology): “Finally, we also collected annotations using the longer contexts present in the GLUE RTE training data, which came from the RTE5 dataset (Bentivogli et al., 2009).”
TinyBERT: Distilling BERT for Natural Language Understanding (2019, background): “, 2016), RTE (Bentivogli et al., 2009) and WNLI (Levesque et al.”
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems (2019, neutral): “, 2007), and RTE5 (Bentivogli et al., 2009).”
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions (2019, background): “also a well studied area of research, particularly on the MultiNLI (Williams et al., 2018) and SNLI (Bowman et al., 2015) datasets. Other sources of entailment data include the PASCAL RTE challenges (Bentivogli et al., 2009, 2011) or SciTail(Khot etal.,2018). Wenote that, although SciTail, RTE-6 and”
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding (2019, background): “, 2018) is a collection of nine NLU tasks, covering textual entailment (RTE (Bentivogli et al., 2009) and MNLI (Williams 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”
Cloze-driven Pretraining of Self-attention Networks (2019, neutral): “, 2007 Bentivogli et al., 2009). We exclude the Winograd NLI task from our results similar to Radford et al. (2018); Devlin et al.”
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers (2020, background): “018), Multi-Genre Natural Language Inference (MNLI) (Williams et al.,2018), Question Natural Language Inference (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 Winograd Natural Language ”
Hypothesis Only Baselines in Natural Language Inference (2018, background): “Technically Bentivogli et al. (2011) was the last challenge under PASCAL’s aegis but Dzikovska et al.”
Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping (2020, background): “, 2018) combines data from a series of datasets (Dagan et al., 2005; Bar-Haim et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., 2009).”