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# Noah A. Smith

13 paper cards in Mova's library · 2009–2020

Noah A. Smith wrote 13 of the papers Mova reads, in 2009–2020, most of them on [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/), [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/), [Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/papers/topics/universal-dependencies/); often with [Nathan Schneider](https://md.mova.elamur.ai/free/en/docs/papers/authors/nathan-schneider/), [Chris Dyer](https://md.mova.elamur.ai/free/en/docs/papers/authors/chris-dyer/), [Lingpeng Kong](https://md.mova.elamur.ai/free/en/docs/papers/authors/lingpeng-kong/).

[OpenAlex](https://openalex.org/A5088517824) · [ORCID](https://orcid.org/0000-0002-2310-6380) · [**h-index 94**](https://openalex.org/authors/A5088517824) · [40232 citations](https://openalex.org/authors/A5088517824) · [486 works](https://openalex.org/works?filter=author.id:A5088517824) — 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=Noah+A.+Smith) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Noah+A.+Smith) · [dblp](https://dblp.org/search?q=Noah+A.+Smith) · [ACL Anthology](https://aclanthology.org/search/?q=Noah+A.+Smith) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Noah+A.+Smith)

Topics: [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/) 2 · [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 2 · [Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/papers/topics/universal-dependencies/) 1 · [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/) 1

## Papers

1. **[Evaluating Models' Local Decision Boundaries via Contrast Sets](https://md.mova.elamur.ai/free/en/docs/papers/gardner-2020-evaluating-models-local-decision-boundaries/)** *(2020)* `measured` Standard test sets come from the same distribution as the training data, so a dataset with systematic gaps or annotation artifacts lets models score well with shallow heuristics.
2. **[Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning](https://md.mova.elamur.ai/free/en/docs/papers/dasigi-2019-quoref-reading-comprehension-dataset-questions/)** *(2019)* Quoref has over 24K span-answer questions on 4.7K Wikipedia paragraphs (about 40% film plot summaries, which mention many characters), written by crowd workers so that answering needs linking two mentions of one entity.
3. **[Parsing Tweets into Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/papers/ud-2018-liu-parsing-tweets-into-universal-dependencies/)** *(2018)* The authors extend the UD guidelines to tweets: emoticons and retweet markers become non-syntactic or discourse tokens, abbreviations such as "idc" stay unnormalized as single words, multiword expressions can be merged, and tweets can have several roots.
4. **[Generation from Abstract Meaning Representation using Tree Transducers](https://md.mova.elamur.ai/free/en/docs/papers/flanigan-2016-generation-abstract-meaning-representation-tree/)** *(2016)* The paper generates English from AMR graphs in two steps. A breadth-first spanning tree is taken from the graph, and a weighted tree-to-string transducer turns that tree into a sentence. The transducer rules are extracted from AMR-aligned sentences: each aligned subgraph becomes a rule with argument slots for its…
5. **[Transition-Based Dependency Parsing with Stack Long Short-Term Memory](https://md.mova.elamur.ai/free/en/docs/papers/missing-2015-dyer-transition-based-dependency-parsing-stack/)** *(2015)* The paper introduces the stack LSTM: an LSTM with a stack pointer, so that pushes and pops take constant time and the network keeps a vector summary of the current stack contents.
6. **[A Corpus and Model Integrating Multiword Expressions and Supersenses](https://md.mova.elamur.ai/free/en/docs/papers/missing-2015-schneider-corpus-model-integrating-multiword-expressions/)** *(2015)* The paper defines one task: find lexical expressions in running text, single- or multi-word, and label the noun and verb ones with a supersense (41 coarse classes).
7. **[A Dependency Parser for Tweets](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2014-kong-dependency-parser-tweets/)** *(2014)* Tweets break the assumptions of newswire parsers: many tokens (retweet markers, usernames, hashtags, URLs, punctuation) have no syntactic role, a tweet can hold several utterances with their own roots, and multiword names and idioms are common.
8. **[An Empirical Comparison of Parsing Methods for Stanford Dependencies](https://md.mova.elamur.ai/free/en/docs/papers/enud-2014-kong-empirical-comparison-parsing-methods-stanford/)** *(2014)* Stanford dependencies are usually obtained by parsing to constituents and then applying head rules (c-parsing), which is slow.
9. **[Comprehensive Annotation of Multiword Expressions in a Social Web Corpus](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2014-schneider-comprehensive-annotation-multiword-expressions-social/)** *(2014)* The authors annotate every multiword expression (MWE) in a web corpus of about 55,000 words, sentence by sentence, without limiting the kinds of MWE.
10. **[Turning on the Turbo: Fast Third-Order Non-Projective Turbo Parsers](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2013-martins-turning-turbo-fast-third-order/)** *(2013)* Non-projective graph parsing with third-order features (grand- and tri-siblings, head bigrams) is NP-hard, so the paper uses AD3, an accelerated dual decomposition algorithm, and extends it with specialised head automata that solve the large subproblems exactly.
11. **[Coarse Lexical Semantic Annotation with Supersenses: An Arabic Case Study](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2012-schneider-coarse-lexical-semantic-annotation-supersenses/)** *(2012)* The authors reuse WordNet's supersense labels (the coarse lexicographer classes) as a light scheme for lexical semantics, and apply it to Arabic.
12. **[Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions](https://md.mova.elamur.ai/free/en/docs/papers/heilman-2010-tree-edit-models-recognizing-textual/)** *(2010)* The paper models a pair of sentences as a sequence of tree edits between their dependency trees. Edits are insert, delete and relabel operations plus three that handle reordering: move a subtree, make a new root, and move a sibling. The sequence is found by greedy best-first search, using a normalised partial tree…
13. **[Feature-Rich Translation by Quasi-Synchronous Lattice Parsing](https://md.mova.elamur.ai/free/en/docs/papers/gimpel-2009-feature-rich-translation-quasi-synchronous/)** *(2009)* The paper presents a log-linear translation model that puts phrase features, target n-gram and dependency-syntax features, and tree-to-tree configuration features into one score.

## Co-authors (51)

- [Nathan Schneider](https://md.mova.elamur.ai/free/en/docs/papers/authors/nathan-schneider/) ×5
- [Chris Dyer](https://md.mova.elamur.ai/free/en/docs/papers/authors/chris-dyer/) ×3
- [Lingpeng Kong](https://md.mova.elamur.ai/free/en/docs/papers/authors/lingpeng-kong/) ×2
- [Matt Gardner](https://md.mova.elamur.ai/free/en/docs/papers/authors/matt-gardner/) ×2
- [Nelson F. Liu](https://md.mova.elamur.ai/free/en/docs/papers/authors/nelson-f-liu/) ×2
- [Pradeep Dasigi](https://md.mova.elamur.ai/free/en/docs/papers/authors/pradeep-dasigi/) ×2
- [Ally Zhang](https://md.mova.elamur.ai/free/en/docs/papers/authors/ally-zhang/)
- [Ana Marasovic](https://md.mova.elamur.ai/free/en/docs/papers/authors/ana-marasovic/)
- Ananth Gottumukkala
- André Martins
- [Archna Bhatia](https://md.mova.elamur.ai/free/en/docs/papers/authors/archna-bhatia/)
- Austin Matthews
- Behrang Mohit
- Ben Bogin
- Ben Zhou
- [Bing Qin](https://md.mova.elamur.ai/free/en/docs/papers/authors/bing-qin/)
- [Daniel Khashabi](https://md.mova.elamur.ai/free/en/docs/papers/authors/daniel-khashabi/)
- [Dheeru Dua](https://md.mova.elamur.ai/free/en/docs/papers/authors/dheeru-dua/)
- Emily Danchik
- Eric Wallace
- Gabriel Ilharco
- [Hannaneh Hajishirzi](https://md.mova.elamur.ai/free/en/docs/papers/authors/hannaneh-hajishirzi/)
- Henrietta Conrad
- [Jaime Carbonell](https://md.mova.elamur.ai/free/en/docs/papers/authors/jaime-carbonell/)
- [Jeffrey Flanigan](https://md.mova.elamur.ai/free/en/docs/papers/authors/jeffrey-flanigan/)
- [Jiangming Liu](https://md.mova.elamur.ai/free/en/docs/papers/authors/jiangming-liu/)
- Jonathan Berant
- Kemal Oflazer
- [Kevin Gimpel](https://md.mova.elamur.ai/free/en/docs/papers/authors/kevin-gimpel/)
- Kevin Lin
- Michael Heilman
- Michael T. Mordowanec
- Miguel Almeida
- Miguel Ballesteros
- Nitish Gupta
- Nora Kazour
- Phoebe Mulcaire
- [Qiang Ning](https://md.mova.elamur.ai/free/en/docs/papers/authors/qiang-ning/)
- [Reut Tsarfaty](https://md.mova.elamur.ai/free/en/docs/papers/authors/reut-tsarfaty/)
- [Sameer Singh](https://md.mova.elamur.ai/free/en/docs/papers/authors/sameer-singh/)
- Sanjay Subramanian
- Sihao Chen
- Spencer Onuffer
- Swabha Swayamdipta
- Victoria Basmov
- Wang Ling
- [Wanxiang Che](https://md.mova.elamur.ai/free/en/docs/papers/authors/wanxiang-che/)
- Yanai Elazar
- Yi Zhu
- [Yijia Liu](https://md.mova.elamur.ai/free/en/docs/papers/authors/yijia-liu/)
- [Yoav Artzi](https://md.mova.elamur.ai/free/en/docs/papers/authors/yoav-artzi/)
- [← previous Nizar Habash](https://md.mova.elamur.ai/free/en/docs/papers/authors/nizar-habash/)
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