The Sixth PASCAL Recognizing Textual Entailment Challenge

By Luisa Bentivogli, Peter Clark, Ido Dagan, Danilo Giampiccolo. 2009

899 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

In Evaluation & Benchmarks

Citations in the corpus

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019, methodology): “RTE Recognizing Textual Entailment is a binary entailment task similar to MNLI, but with much less training data (Bentivogli et al., 2009).”
RoBERTa: A Robustly Optimized BERT Pretraining Approach (2019, background): “Our model establishes a new state-of-the-art on 4/9 of the GLUE tasks: MNLI, QNLI, RTE and STS-B.”
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).”
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).”
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).”
TinyBERT: Distilling BERT for Natural Language Understanding (2019, background): “RTE.”
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).”
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions (2019, background): “Other sources of entailment data include the PASCAL RTE challenges (Bentivogli et al., 2009, 2011) or Sci-Tail (Khot et al., 2018).”
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, 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, background): “We present the results for such intermediate-multi-task training on RTE in Table 3.”
Cloze-driven Pretraining of Self-attention Networks (2019, background): “…inference tasks: the Multi-Genre Natural Language Inference (MNLI; Williams et al., 2018), the Stanford Question Answering Dataset (QNLI; Rajpurkar et al., 2016), the Recognizing Textual Entailment (RTE; Dagan et al., 2006, Bar Haim et al., 2006, Ciampiccolo et al., 2007 Bentivogli et al., 2009).”
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): “…• 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 al., 2012; Wang et al., 2019b) A.5…”
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.”