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Dan Klein

9 paper cards in Mova's library · 2003–2020

Dan Klein wrote 9 of the papers Mova reads, in 2003–2020, most of them on Coreference resolution, Machine translation; often with Daniel Fried, Jonathan K. Kummerfeld, Nikita Kitaev.

OpenAlex · ORCID · h-index 76 · 26397 citations · 409 works — 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 · Semantic Scholar · dblp · ACL Anthology · ORCID

Topics: Coreference resolution 3 · Machine translation 1

Papers

  1. Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference (2020)
    The paper parses constituency trees by labeling each word with one of four "tetra-tags" that encode two binary structural decisions and two labels.
  2. Pragmatically Informative Text Generation (2019)
    Conditional generators often under-inform: a restaurant description may leave out attributes of its input meaning representation.
  3. Cross-Domain Generalization of Neural Constituency Parsers (2019)
    The paper trains constituency parsers on the WSJ and tests them zero-shot on Brown, Genia and the English Web Treebank, and on Chinese treebank domains.
  4. Easy Victories and Uphill Battles in Coreference Resolution (2013)
    The paper builds a learning-based coreference system, the Berkeley system, that uses only a small set of homogeneous feature templates over shallow mention properties (head match, exact string match, and syntactic and discourse features) instead of many hand-built heuristics.
  5. Error-Driven Analysis of Challenges in Coreference Resolution (2013)
    The paper gives an automatic classifier of coreference errors that works only on system output, so it applies to link-based and global-inference systems alike.
  6. Parser Showdown at the Wall Street Corral: An Empirical Investigation of Error Types in Parser Output (2012)
    A single F-score on WSJ section 23 says nothing about which errors a constituency parser makes. The authors repair each wrong parse into the gold tree with a few tree transformations (subtree movement, node creation, node deletion), and name each repair by its linguistic type: PP attachment, clause attachment…
  7. Simple Coreference Resolution with Rich Syntactic and Semantic Features (2009)
    The paper shows that a deliberately simple, deterministic coreference system can compete with much more complex learned models if it uses good syntactic and semantic information.
  8. Learning Accurate, Compact, and Interpretable Tree Annotation (2006)
    Hand-made tree annotations, which split a category such as NP into subject and object NPs, raise parser accuracy, but they are made by people for one language.
  9. Accurate Unlexicalized Parsing (2003)
    The paper asks how far a PCFG with no lexical items can go on the Penn Treebank. A vanilla treebank grammar parses badly because its independence assumptions are too strong. Adding simple linguistic state splits, such as parent annotation, marking of base noun phrases, tag splits for function words and horizontal…

Co-authors (13)

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