The CommitmentBank: Investigating projection in naturally occurring discourse

By Marie-Catherine de Marneffe, Mandy Simons, Judith Tonhauser. 2019

456 citations recorded by the source corpus.

Subfields: Language and Linguistics, Literature and Literary Theory, Natural Language Processing, Philosophy

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Citations in the corpus

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems (2019, methodology): “We then collapse the annotations into 3 classes by using the same ranges for bucketing used by de Marneffe et al. (2019).”
Finetuned Language Models Are Zero-Shot Learners (2021, background): “Despite high variance among different templates for CB (De Marneffe et al., 2019) and RTE (Wang et al., 2018), FLAN outperforms zero and few-shot GPT-3 for four datasets by a large margin without any prompt engineering.”
The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only (2023, neutral): “Tasks Type Random small core main ext HellaSwag (Zellers et al., 2019) Sentence completion 25.0 ✓ ✓ ✓ ✓ LAMBADA (Paperno et al., 2016) Sentence completion 0.0 ✓ ✓ ✓ Winogrande (Sakaguchi et al., 2021) Coreference resolution 50.0 ✓ ✓ ✓ ✓ PIQA (Bisk et al., 2020) Multiple-choice question answering 50.”
How many data points is a prompt worth? (2021, background): “These datasets comprise a variety of tasks, all in English, including entailment (MNLI, RTE (Dagan et al., 2005), CB (de Marneffe et al., 2019)), multiple choice question answering (BoolQ (Clark et al., 2019), MultiRC (Khashabi et al., 2018)), and commonsense reasoning (WSC (Levesque et al., 2012),…”
It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners (2020, methodology): “…deﬁne two verbalizers mapping questions containing a true statement to yes / true and others to no / false , respectively, for a total of 6 PVPs. CB (De Marneffe et al., 2019) and RTE (Dagan et al., 2006) are textual entailment tasks like MNLI, so we use PVPs similar to Schick and Sch¨utze (2020a).”
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? (2022, neutral): “…(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), ethos-national_origin (Mollas et al., 2020),…”
UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark (2021, background): “Speciﬁcally, S UPER GLUE comprises tasks for identifying when speakers implicitly assert something (De Marneffe, Simons, and Tonhauser 2019), determining cause-effect relationships (Roemmele, Bejan, and Gordon 2011), reading comprehension (Khashabi et al. 2018; Zhang et al. 2018), natural language…”
HTLM: Hyper-Text Pre-Training and Prompting of Language Models (2021, methodology): “We select the same datasets that were used in the original paper for our experimentation; MNLI (Williams et al., 2018), BoolQ (Clark et al., 2019), CB (De Marneffe et al., 2019), RTE (Bentivogli et al., 2009), WiC (Pilehvar and Camacho-Collados, 2019).”