--- title: "R. Thomas McCoy — papers and cards" description: "R. Thomas McCoy: 2 paper cards in the Research library (2019–2026), with what each paper is about, the ideas Mova took from it, and the co-authors." canonical: "https://research.elamur.ai/free/en/authors/r-thomas-mccoy/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/r-thomas-mccoy/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / R. Thomas McCoy # R. Thomas McCoy 2 paper cards in the Research library · 2019–2026 R. Thomas McCoy wrote 2 of the papers Mova reads, in 2019–2026; often with Claire Hobbs, [Ellie Pavlick](https://md.research.elamur.ai/free/en/authors/ellie-pavlick/), [Tal Linzen](https://md.research.elamur.ai/free/en/authors/tal-linzen/). [OpenAlex](https://openalex.org/A5003082850) · [ORCID](https://orcid.org/0000-0001-9383-4079) · [**h-index 15**](https://openalex.org/authors/A5003082850) · [1048 citations](https://openalex.org/authors/A5003082850) · [72 works](https://openalex.org/works?filter=author.id:A5003082850) — openalex-snapshot, 2026-10-14 The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=R.+Thomas+McCoy) · [Semantic Scholar](https://www.semanticscholar.org/search?q=R.+Thomas+McCoy) · [dblp](https://dblp.org/search?q=R.+Thomas+McCoy) · [ACL Anthology](https://aclanthology.org/search/?q=R.+Thomas+McCoy) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=R.+Thomas+McCoy) ## Papers 1. **[Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks](https://md.research.elamur.ai/free/en/papers/hobbs-2026-collocational-bootstrapping-hypothesis-about-learning/)** *(2026)* The paper asks how statistical regularities in linguistic input could help a learner acquire syntax. It proposes "collocational bootstrapping": which words tend to occur together hints at which words are syntactically related. The test case is English subject-verb number agreement. In most natural sentences the… 2. **[Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/mccoy-2019-right-wrong-reasons-diagnosing-syntactic/)** *(2019)* Statistical NLI models can score high on MNLI while relying on three syntactic shortcuts: lexical overlap (a premise entails any hypothesis built from its words), the subsequence heuristic (it entails any contiguous word span), and the constituent heuristic (it entails any complete subtree). ## Co-authors (3) - Claire Hobbs - [Ellie Pavlick](https://md.research.elamur.ai/free/en/authors/ellie-pavlick/) - [Tal Linzen](https://md.research.elamur.ai/free/en/authors/tal-linzen/) ## More papers in the library (4) Pages of the Research library: our summary, the ideas, references and who cites each. - [Does Syntax Need to Grow on Trees? Sources of Hierarchical Inductive Bias in Sequence-to-Sequence Networks](https://md.research.elamur.ai/free/en/papers/mccoy-2020-does-syntax-need-grow-trees/) — R. Thomas McCoy, Robert Frank, Tal Linzen · 2020 - [Syntactic Data Augmentation Increases Robustness to Inference Heuristics](https://md.research.elamur.ai/free/en/papers/min-2020-syntactic-data-augmentation-increases-robustness/) — Junghyun Min, R. Thomas McCoy, Dipanjan Das, et al. · 2020 - [Probing What Different NLP Tasks Teach Machines about Function Word Comprehension](https://md.research.elamur.ai/free/en/papers/kim-2019-probing-what-different-nlp-tasks/) — Najoung Kim, Roma Patel, Adam Poliak, et al. · 2019 - [What do you learn from context? Probing for sentence structure in contextualized word representations](https://md.research.elamur.ai/free/en/papers/tenney-2019-learn-context-probing-sentence-structure/) — Ian Tenney, Patrick Xia, Berlin Chen, et al. · 2019