The Second PASCAL Recognising Textual Entailment Challenge

By Roy Bar-Haim, Ido Dagan, Bill Dolan, Lisa Ferro, Danilo Giampiccolo, Bernardo Magnini, Idan Szpektor. 2006

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Adversarial Training for Large Neural Language Models (2020, background): “…analysis (Socher et al., 2013), text similarity (Cer et al., 2017), paraphrase detection (Dolan and Brockett, 2005), and natural language inference (NLI) (Da-gan et al., 2006; Bar-Haim et al., 2006; Giampic-colo et al., 2007; Bentivogli et al., 2009; Levesque et al., 2012; Williams et al., 2018).”
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 is included in GLUE, and we use the same data and format as GLUE: 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).”
SpanBERT: Improving Pre-training by Representing and Predicting Spans (2019, methodology): “%) and in RTE (+6.9%), the latter accounting for most of the rise in SpanBERT’s GLUE average.”
Unified Language Model Pre-training for Natural Language Understanding and Generation (2019, background): “The dropout rate of the last linear projection for each task is set to 0.1, except 0.3 for MNLI and 0.05 for CoLA/SST-2.”
Unifying Question Answering and Text Classification via Span Extraction (2019, methodology): “This is especially pronounced for datasets with limited training data, such as MRPC and RTE with SEBert LARGE and BERT LARGE : 85.2 vs 83.4 for RTE and 90.4 vs 89.5 for MRPC).”
Self-Consistency Improves Chain of Thought Reasoning in Language Models (2022, background): “…including (1) Closed-Book Question Answering: BoolQ (Clark et al., 2019), HotpotQA (Yang et al., 2018), and (2) Natural Language Inference: e-SNLI (Camburu et al., 2018), ANLI (Nie et al., 2020) and RTE (Dagan et al., 2005; Bar-Haim et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., 2009).”
Finetuned Language Models Are Zero-Shot Learners (2021, background): “RTE (Dagan et al., 2005; Haim et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., 2009) • Reading comprehension tests the ability to answer a question when given a passage that contains the answer.”
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus (2021, neutral): “…• MNLI (Williams et al., 2018) • MRPC (Dolan and Brockett, 2005) • QNLI (Rajpurkar et al., 2016; Wang et al., 2019b) • RTE (Dagan et al., 2005; Haim et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., 2009) • SST-2 (Socher et al., 2013) • STS-B (Cer et al., 2017) • WNLI (Levesque et…”
DeBERTa: Decoding-enhanced BERT with Disentangled Attention (2020, background): “0 [32], RACE [33], ReCoRD [34] and SWAG [35]; (2) Natural Language Inference: MNLI [36]; and (3) NER: CoNLL-2003.”
The Power of Scale for Parameter-Efficient Prompt Tuning (2021, background): “, 2018), RTE (Dagan et al., 2005; Bar-Haim et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., 2009), WiC (Pilehvar and CamachoCollados, 2018), and WSC (Levesque et al.”
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? (2022, neutral): “…glue-mrpc (Dolan and Brockett, 2005), glue-wnli (Levesque et al., 2012), climate_fever (Diggel-mann et al., 2020), glue-rte (Dagan et al., 2005; Bar-Haim et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., 2009), superglue-cb (de Marneffe et al., 2019), sick (Marelli et al., 2014) ,…”
UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark (2021, background): “…language inference (sometimes constructed from other datasets) (Williams, Nangia, and Bowman 2018; Rajpurkar et al. 2016; Dagan, Glickman, and Magnini 2006; Bar Haim et al. 2006; Giampiccolo et al. 2007; Bentivogli et al. 2009; Levesque, Davis, and Morgenstern 2011), and general diagnostics.”
Rationale-Augmented Ensembles in Language Models (2022, methodology): “…Language Inference : For these tasks, we include ANLI (Nie et al., 2020) with the three subsets (R1, R2, R3), e-SNLI (Camburu et al., 2018), MNLI (matched/mis-matched) (Williams et al., 2018), and RTE (Dagan et al., 2005; Bar-Haim et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., 2009).”