The Fourth PASCAL Recognizing Textual Entailment Challenge

By Danilo Giampiccolo, Hoa Trang Dang, Bernardo Magnini, Ido Dagan, Elena Cabrio, William B. Dolan. 2008

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In Natural Language Processing

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).6 WNLI Winograd NLI is a small natural language inference dataset deriving from (Levesque et al., 2011).”
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).”
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding (2019, background): “…General Language Understanding Evaluation (GLUE) benchmark [13] is a collection of nine NLU tasks as in Table 1, covering textual entailment (RTE [26] and MNLI [27]), question-answer entailment (QNLI [13]), paraphrase (MRPC [28]), question paraphrase (QQP), textual similarity (STS-B [3]),…”
Unifying Question Answering and Text Classification via Span Extraction (2019, methodology): “The summation of cross-entropy losses over the start and end distributions then gives an overall loss for a training example: At inference, we extract a span ( a, b ) as Bar-Haim et al., 2006; Giampic-colo et al., 2007; Bentivogli et al., 2009).”
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).”
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus (2021, neutral): “…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…”
Making Pre-trained Language Models Better Few-shot Learners (2021, methodology): “…al., 2019), MNLI (Williams et al., 2018), QNLI (Rajpurkar et al., 2016), RTE (Dagan et al., 2005; Bar Haim et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., 2009), MRPC (Dolan and Brockett, 2005), QQP12 and STS-B (Cer et al., 2017), we follow Zhang et al. (2021) and use their original…”
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? (2022, neutral): “…(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) , hate_speech18 (de Gibert et al., 2018),…”
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).”