Scholarship, Research, and Creative Work at Bryn Mawr College Scholarship, Research, and Creative Work at Bryn Mawr College

By Brittany L. Smith, Hannah Schoenberg, S. V. Koebele, Laura Grafe

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SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems (2019, methodology): “The outside results for COPA, MultiRC, and RTE are from Sap et al. (2019), Trivedi et al.”
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference (2019, background): “Several past works (Pavlick and Callison-Burch, 2016; Rudinger et al., 2018; White et al., 2018; White and Rawlins, 2018) have studied such behavior for verbs (e.g., He knows it is raining entails It is raining , while He believes it is raining does not).”
The Natural Language Decathlon: Multitask Learning as Question Answering (2018, background): “By unifying tasks under a single perspective, it is possible to explore these relationships [Wang et al., 2018, Poliak et al., 2018a,b].”
Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender Bias (2022, methodology): “We use the casting of Winogender as an NLI task (Poliak et al., 2018), which is part of the SuperGLUE benchmark (Wang et al., 2019).”
UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark (2021, background): “…et al. 2018), natural language inference (Dagan, Glickman, and Magnini 2006; Bar Haim et al. 2006; Giampiccolo et al. 2007; Ben-tivogli et al. 2009; Poliak et al. 2018), word sense disam-biguation (Pilehvar and Camacho-Collados 2019), winograd schemas (Levesque, Davis, and Morgenstern 2011),…”
BERT Rediscovers the Classical NLP Pipeline (2019, methodology): “Much of this work is behavior-based, designing controlled test sets and analyzing errors in order to reverse-engineer the types of abstractions the model may or may not be representing (e.g. Con-neau et al., 2018; Marvin and Linzen, 2018; Poliak et al., 2018).”
What do you learn from context? Probing for sentence structure in contextualized word representations (2019, methodology): “In the DNC, these tasks are all recast into natural language inference (NLI) format (White et al., 2017), i.e. systems must understand the targeted semantic phenomenon in order to make correct inferences about en-9 tailment.”
An Empirical Investigation of the Role of Pre-training in Lifelong Learning (2021, methodology): “The factuality annotations from the Decomp corpus are recast into an NLI structure and we use the modiﬁed dataset from Diverse NLI Collection (Poliak et al., 2018).”
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension (2019, methodology): “Using recast NLI, Poliak et al. (2018a) probe for semantic phenomena in neural machine translation encoders.”
Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start (2020, background): “…in Bowman et al. (2015)) is the task of studying the relation of two assertive sentences, Premise (P) and Hypothesis (H): whether H is true given P. Textual entailment (TE) was originally brought up as a uniﬁed framework for modeling diverse NLP tasks (Dagan et al., 2005; Poliak et al., 2018).”
Probing Natural Language Inference Models through Semantic Fragments (2019, background): “…increased scrutiny of systematic annotation biases in existing datasets (Poliak et al. 2018b; Gururangan et al. 2018), as well as attempts to build new challenge datasets that focus on particular linguistic phenomena (Glockner, Shwartz, and Goldberg 2018; Naik et al. 2018; Poliak et al. 2018a).”
Diversify Your Datasets: Analyzing Generalization via Controlled Variance in Adversarial Datasets (2019, background): “that neural models often fail on particular inference types, like recognizing semantic relations and negation (Poliak et al., 2018; Naik et al., 2018; Glockner et al., 2018).”
Transforming Question Answering Datasets Into Natural Language Inference Datasets (2018, positive): “Although White et al. (2017) and Poliak et al. (2018) have explored recasting datasets created for various semantic classiﬁcation tasks (e.g. semantic role labeling and named entity recognition) into NLI datasets, we are the ﬁrst to perform such an automated conversion on QA datasets.”
Evidence-based Verification for Real World Information Needs (2021, methodology): “Following (Poliak et al., 2018), we trained a clas-siﬁer to predict claim veracity without evidence.”
Interpretability and Analysis in Neural NLP (2020, background): “…alia), controlled behavior studies on language modelling (Gulordava et al., 2018; Linzen et al., 2016a; Mar-vin and Linzen, 2018) or inference tasks (Poliak et al., 2018a,b; White et al., 2017; Kim et al., 2019; McCoy et al., 2019; Ross and Pavlick, 2019), psycholinguistic methods (Ettinger et…”