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# Mark Yatskar

2 paper cards in Mova's library · 2017–2023

Mark Yatskar wrote 2 of the papers Mova reads, in 2017–2023, most of them on [Coreference resolution](https://md.mova.elamur.ai/free/en/docs/papers/topics/coreference-resolution/), [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/); often with [Chaitanya Malaviya](https://md.mova.elamur.ai/free/en/docs/papers/authors/chaitanya-malaviya/), Ioannis Konstas, [Luke Zettlemoyer](https://md.mova.elamur.ai/free/en/docs/papers/authors/luke-zettlemoyer/).

[OpenAlex](https://openalex.org/A5039205261) · [**h-index 29**](https://openalex.org/authors/A5039205261) · [7636 citations](https://openalex.org/authors/A5039205261) · [81 works](https://openalex.org/works?filter=author.id:A5039205261) — openalex, 2026-10-18

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=Mark+Yatskar) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Mark+Yatskar) · [dblp](https://dblp.org/search?q=Mark+Yatskar) · [ACL Anthology](https://aclanthology.org/search/?q=Mark+Yatskar) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Mark+Yatskar)

Topics: [Coreference resolution](https://md.mova.elamur.ai/free/en/docs/papers/topics/coreference-resolution/) 1 · [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 1

## Papers

1. **[AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference](https://md.mova.elamur.ai/free/en/docs/papers/yuan-2023-ambicoref-evaluating-human-model-sensitivity/)** *(2023)* The paper asks whether coreference models react to pronoun ambiguity as people do. It builds AmbiCoref, a corpus of minimal sentence pairs: an ambiguous and an unambiguous sentence differ only in one verb phrase (for example "Abby told Brittney that she upset Courtney" against "hugged"). The templates come from four…
2. **[Neural AMR: Sequence-to-Sequence Models for Parsing and Generation](https://md.mova.elamur.ai/free/en/docs/papers/konstas-2017-neural-amr-sequence-sequence-models/)** *(2017)* The paper applies sequence-to-sequence neural models to both AMR parsing and AMR generation. The main obstacle is data sparsity, so the authors pretrain on millions of unlabelled Gigaword sentences: a parser labels them, a generator is pretrained on those pairs, and then both are fine-tuned on the 16,833 gold pairs.…

## Co-authors (6)

- [Chaitanya Malaviya](https://md.mova.elamur.ai/free/en/docs/papers/authors/chaitanya-malaviya/)
- Ioannis Konstas
- [Luke Zettlemoyer](https://md.mova.elamur.ai/free/en/docs/papers/authors/luke-zettlemoyer/)
- Srinivasan Iyer
- [Yejin Choi](https://md.mova.elamur.ai/free/en/docs/papers/authors/yejin-choi/)
- Yuewei Yuan
- [← previous Mark Steedman](https://md.mova.elamur.ai/free/en/docs/papers/authors/mark-steedman/)
- [next → Markus Dickinson](https://md.mova.elamur.ai/free/en/docs/papers/authors/markus-dickinson/)
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