---
title: "Dan Klein — papers and cards"
description: "Dan Klein: 9 paper cards in the Research library (2003–2020), with what each paper is about, the ideas Mova took from it, and the co-authors."
canonical: "https://research.elamur.ai/free/en/authors/dan-klein/"
---
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# Dan Klein

9 paper cards in the Research library · 2003–2020

Dan Klein wrote 9 of the papers Mova reads, in 2003–2020, most of them on [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/), [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/); often with [Daniel Fried](https://md.research.elamur.ai/free/en/authors/daniel-fried/), [Jonathan K. Kummerfeld](https://md.research.elamur.ai/free/en/authors/jonathan-k-kummerfeld/), [Nikita Kitaev](https://md.research.elamur.ai/free/en/authors/nikita-kitaev/).

[OpenAlex](https://openalex.org/A5004921249) · [ORCID](https://orcid.org/0000-0002-8881-1902) · [**h-index 74**](https://openalex.org/authors/A5004921249) · [25166 citations](https://openalex.org/authors/A5004921249) · [293 works](https://openalex.org/works?filter=author.id:A5004921249) — 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=Dan+Klein) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Dan+Klein) · [dblp](https://dblp.org/search?q=Dan+Klein) · [ACL Anthology](https://aclanthology.org/search/?q=Dan+Klein) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Dan+Klein)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) 3 · [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) 1

## Papers

1. **[Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference](https://md.research.elamur.ai/free/en/papers/acl-2020-kitaev-tetra-tagging-word-synchronous-parsing/)** *(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](https://md.research.elamur.ai/free/en/papers/shen-2019-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](https://md.research.elamur.ai/free/en/papers/acl-2019-fried-cross-domain-generalization-neural-constituency/)** *(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](https://md.research.elamur.ai/free/en/papers/durrett-2013-easy-victories-uphill-battles-coreference/)** *(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](https://md.research.elamur.ai/free/en/papers/kummerfeld-2013-error-driven-analysis-challenges-coreference/)** *(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](https://md.research.elamur.ai/free/en/papers/aclbulk-2012-kummerfeld-parser-showdown-wall-street-corral/)** *(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](https://md.research.elamur.ai/free/en/papers/haghighi-2009-simple-coreference-resolution-rich-syntactic/)** *(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](https://md.research.elamur.ai/free/en/papers/aclbulk-2006-petrov-learning-accurate-compact-interpretable-tree/)** *(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](https://md.research.elamur.ai/free/en/papers/aclbulk-2003-klein-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)

- [Daniel Fried](https://md.research.elamur.ai/free/en/authors/daniel-fried/) ×2
- [Jonathan K. Kummerfeld](https://md.research.elamur.ai/free/en/authors/jonathan-k-kummerfeld/) ×2
- [Nikita Kitaev](https://md.research.elamur.ai/free/en/authors/nikita-kitaev/) ×2
- Aria Haghighi
- [Christopher D. Manning](https://md.research.elamur.ai/free/en/authors/christopher-d-manning/)
- David Hall
- [Greg Durrett](https://md.research.elamur.ai/free/en/authors/greg-durrett/)
- [Jacob Andreas](https://md.research.elamur.ai/free/en/authors/jacob-andreas/)
- [James R. Curran](https://md.research.elamur.ai/free/en/authors/james-r-curran/)
- Leon Barrett
- Romain Thibaux
- Sheng Shen
- [Slav Petrov](https://md.research.elamur.ai/free/en/authors/slav-petrov/)

## More papers in the library (24)

Pages of the Research library: our summary, the ideas, references and who cites each.

- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2021-calibrate-before-use-improving-few/) — Tony Z. Zhao, Eric Wallace, Shi Feng, et al. · 2021
- [FUDGE: Controlled Text Generation With Future Discriminators](https://md.research.elamur.ai/free/en/papers/yang-2021-fudge-controlled-text-generation-future/) — Kevin Yang, Dan Klein · 2021
- [Multilingual Constituency Parsing with Self-Attention and Pre-Training](https://md.research.elamur.ai/free/en/papers/kitaev-2019-multilingual-constituency-parsing-self-attention/) — Nikita Kitaev, Steven Cao, Dan Klein · 2019
- [Constituency Parsing with a Self-Attentive Encoder](https://md.research.elamur.ai/free/en/papers/kitaev-2018-constituency-parsing-self-attentive-encoder/) — Nikita Kitaev, Dan Klein · 2018
- [Unified Pragmatic Models for Generating and Following Instructions](https://md.research.elamur.ai/free/en/papers/fried-2018-unified-pragmatic-models-generating-following/) — Daniel Fried, Jacob Andreas, Dan Klein · 2018
- [A Minimal Span-Based Neural Constituency Parser](https://md.research.elamur.ai/free/en/papers/stern-2017-minimal-span-based-neural-constituency/) — Mitchell Stern, Jacob Andreas, Dan Klein · 2017
- [Learning to Compose Neural Networks for Question Answering](https://md.research.elamur.ai/free/en/papers/andreas-2016-learning-compose-neural-networks-question/) — Jacob Andreas, Marcus Rohrbach, Trevor Darrell, et al. · 2016
- [Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints](https://md.research.elamur.ai/free/en/papers/durrett-2016-learning-based-single-document-summarization/) — Greg Durrett, Taylor Berg-Kirkpatrick, Dan Klein · 2016
- [Reasoning about Pragmatics with Neural Listeners and Speakers](https://md.research.elamur.ai/free/en/papers/andreas-2016-reasoning-about-pragmatics-neural-listeners/) — Jacob Andreas, Dan Klein · 2016
- [Neural Module Networks](https://md.research.elamur.ai/free/en/papers/andreas-2015-neural-module-networks/) — Jacob Andreas, Marcus Rohrbach, Trevor Darrell, et al. · 2015
- [A Joint Model for Entity Analysis: Coreference, Typing, and Linking](https://md.research.elamur.ai/free/en/papers/durrett-2014-joint-model-entity-analysis-coreference/) — Greg Durrett, Dan Klein · 2014
- [Decentralized Entity-Level Modeling for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/durrett-2013-decentralized-entity-level-modeling-coreference/) — Greg Durrett, David Hall, Dan Klein · 2013
- [Coreference Semantics from Web Features](https://md.research.elamur.ai/free/en/papers/bansal-2012-coreference-semantics-web-features/) — Mohit Bansal, Dan Klein · 2012
- [A Simple Domain-Independent Probabilistic Approach to Generation](https://md.research.elamur.ai/free/en/papers/angeli-2010-simple-domain-independent-probabilistic-approach/) — Gabor Angeli, Percy Liang, Dan Klein · 2010
- [Coreference Resolution in a Modular, Entity-Centered Model](https://md.research.elamur.ai/free/en/papers/haghighi-2010-coreference-resolution-modular-entity-centered/) — Aria Haghighi, Dan Klein · 2010
- [Better Word Alignments with Supervised ITG Models](https://md.research.elamur.ai/free/en/papers/haghighi-2009-better-word-alignments-supervised-itg/) — Aria Haghighi, John Blitzer, John DeNero, et al. · 2009
- [Learning Semantic Correspondences with Less Supervision](https://md.research.elamur.ai/free/en/papers/liang-2009-learning-semantic-correspondences-less-supervision/) — Percy Liang, Michael Jordan, Dan Klein · 2009
- [Learning Bilingual Lexicons from Monolingual Corpora](https://md.research.elamur.ai/free/en/papers/haghighi-2008-learning-bilingual-lexicons-monolingual-corpora/) — Aria Haghighi, Percy Liang, Taylor Berg-Kirkpatrick, et al. · 2008
- [Improved Inference for Unlexicalized Parsing](https://md.research.elamur.ai/free/en/papers/petrov-2007-improved-inference-unlexicalized-parsing/) — Slav Petrov, Dan Klein · 2007
- [Unsupervised Coreference Resolution in a Nonparametric Bayesian Model](https://md.research.elamur.ai/free/en/papers/haghighi-2007-unsupervised-coreference-resolution-nonparametric-bayesian/) — Aria Haghighi, Dan Klein · 2007
- [Alignment by Agreement](https://md.research.elamur.ai/free/en/papers/liang-2006-alignment-agreement/) — Percy Liang, Ben Taskar, Dan Klein · 2006
- [A Discriminative Matching Approach to Word Alignment](https://md.research.elamur.ai/free/en/papers/taskar-2005-discriminative-matching-approach-word-alignment/) — Ben Taskar, Simon Lacoste-Julien, Dan Klein · 2005
- [Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network](https://md.research.elamur.ai/free/en/papers/toutanova-2003-feature-rich-part-speech-tagging/) — Kristina Toutanova, Dan Klein, Christopher D. Manning, et al. · 2003
- [A Generative Constituent-Context Model for Improved Grammar Induction](https://md.research.elamur.ai/free/en/papers/klein-2002-generative-constituent-context-model-improved/) — Dan Klein, Christopher D. Manning · 2002