---
title: "Noah A. Smith — papers and cards"
description: "Noah A. Smith: 13 paper cards in the Research library (2009–2020), with what each paper is about, the ideas Mova took from it, and the co-authors."
canonical: "https://research.elamur.ai/free/en/authors/noah-a-smith/"
---
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# Noah A. Smith

13 paper cards in the Research library · 2009–2020

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

[OpenAlex](https://openalex.org/A5088517824) · [ORCID](https://orcid.org/0000-0002-2310-6380) · [**h-index 93**](https://openalex.org/authors/A5088517824) · [38892 citations](https://openalex.org/authors/A5088517824) · [484 works](https://openalex.org/works?filter=author.id:A5088517824) — 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=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.research.elamur.ai/free/en/topics/dependency-parsing/) 2 · [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) 2 · [Universal Dependencies](https://md.research.elamur.ai/free/en/topics/universal-dependencies/) 1 · [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/) 1

## Papers

1. **[Evaluating Models' Local Decision Boundaries via Contrast Sets](https://md.research.elamur.ai/free/en/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.research.elamur.ai/free/en/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.research.elamur.ai/free/en/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.research.elamur.ai/free/en/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.research.elamur.ai/free/en/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.research.elamur.ai/free/en/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.research.elamur.ai/free/en/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. **[Comprehensive Annotation of Multiword Expressions in a Social Web Corpus](https://md.research.elamur.ai/free/en/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.
9. **[An Empirical Comparison of Parsing Methods for Stanford Dependencies](https://md.research.elamur.ai/free/en/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.
10. **[Turning on the Turbo: Fast Third-Order Non-Projective Turbo Parsers](https://md.research.elamur.ai/free/en/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.research.elamur.ai/free/en/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.research.elamur.ai/free/en/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.research.elamur.ai/free/en/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.research.elamur.ai/free/en/authors/nathan-schneider/) ×5
- [Chris Dyer](https://md.research.elamur.ai/free/en/authors/chris-dyer/) ×3
- [Lingpeng Kong](https://md.research.elamur.ai/free/en/authors/lingpeng-kong/) ×2
- [Matt Gardner](https://md.research.elamur.ai/free/en/authors/matt-gardner/) ×2
- [Nelson F. Liu](https://md.research.elamur.ai/free/en/authors/nelson-f-liu/) ×2
- [Pradeep Dasigi](https://md.research.elamur.ai/free/en/authors/pradeep-dasigi/) ×2
- [Ally Zhang](https://md.research.elamur.ai/free/en/authors/ally-zhang/)
- [Ana Marasovic](https://md.research.elamur.ai/free/en/authors/ana-marasovic/)
- Ananth Gottumukkala
- André Martins
- [Archna Bhatia](https://md.research.elamur.ai/free/en/authors/archna-bhatia/)
- Austin Matthews
- Behrang Mohit
- Ben Bogin
- Ben Zhou
- [Bing Qin](https://md.research.elamur.ai/free/en/authors/bing-qin/)
- [Daniel Khashabi](https://md.research.elamur.ai/free/en/authors/daniel-khashabi/)
- [Dheeru Dua](https://md.research.elamur.ai/free/en/authors/dheeru-dua/)
- Emily Danchik
- Eric Wallace
- Gabriel Ilharco
- [Hannaneh Hajishirzi](https://md.research.elamur.ai/free/en/authors/hannaneh-hajishirzi/)
- Henrietta Conrad
- [Jaime Carbonell](https://md.research.elamur.ai/free/en/authors/jaime-carbonell/)
- [Jeffrey Flanigan](https://md.research.elamur.ai/free/en/authors/jeffrey-flanigan/)
- [Jiangming Liu](https://md.research.elamur.ai/free/en/authors/jiangming-liu/)
- Jonathan Berant
- Kemal Oflazer
- [Kevin Gimpel](https://md.research.elamur.ai/free/en/authors/kevin-gimpel/)
- Kevin Lin
- Michael Heilman
- Michael T. Mordowanec
- Miguel Almeida
- Miguel Ballesteros
- Nitish Gupta
- Nora Kazour
- Phoebe Mulcaire
- [Qiang Ning](https://md.research.elamur.ai/free/en/authors/qiang-ning/)
- [Reut Tsarfaty](https://md.research.elamur.ai/free/en/authors/reut-tsarfaty/)
- [Sameer Singh](https://md.research.elamur.ai/free/en/authors/sameer-singh/)
- Sanjay Subramanian
- Sihao Chen
- Spencer Onuffer
- Swabha Swayamdipta
- Victoria Basmov
- Wang Ling
- [Wanxiang Che](https://md.research.elamur.ai/free/en/authors/wanxiang-che/)
- Yanai Elazar
- Yi Zhu
- [Yijia Liu](https://md.research.elamur.ai/free/en/authors/yijia-liu/)
- [Yoav Artzi](https://md.research.elamur.ai/free/en/authors/yoav-artzi/)

## More papers in the library (43)

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

- [Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2](https://md.research.elamur.ai/free/en/papers/ivison-2023-camels-changing-climate-enhancing-lm/) — Hamish Ivison, Yizhong Wang, Valentina Pyatkin, et al. · 2023
- [Fine-Grained Human Feedback Gives Better Rewards for Language Model Training](https://md.research.elamur.ai/free/en/papers/wu-2023-fine-grained-human-feedback-gives/) — Zeqiu Wu, Yushi Hu, Weijia Shi, et al. · 2023
- [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://md.research.elamur.ai/free/en/papers/wang-2023-far-camels-go-exploring-state/) — Yizhong Wang, Hamish Ivison, Pradeep Dasigi, et al. · 2023
- [How Language Model Hallucinations Can Snowball](https://md.research.elamur.ai/free/en/papers/zhang-2023-language-model-hallucinations-snowball/) — Muru Zhang, Ofir Press, William Merrill, et al. · 2023
- [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://md.research.elamur.ai/free/en/papers/sap-2022-annotators-attitudes-how-annotator-beliefs/) — Maarten Sap, Swabha Swayamdipta, Laura Vianna, et al. · 2022
- [Binding Language Models in Symbolic Languages](https://md.research.elamur.ai/free/en/papers/cheng-2022-binding-language-models-symbolic-languages/) — Zhoujun Cheng, Tianbao Xie, Peng Shi, et al. · 2022
- [In-Context Learning for Few-Shot Dialogue State Tracking](https://md.research.elamur.ai/free/en/papers/hu-2022-context-learning-few-shot-dialogue/) — Yushi Hu, Chia-Hsuan Lee, Tianbao Xie, et al. · 2022
- [NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics](https://md.research.elamur.ai/free/en/papers/lu-2022-neurologic-esque-decoding-constrained-text/) — Ximing Lu, Sean Welleck, Peter West, et al. · 2022
- [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://md.research.elamur.ai/free/en/papers/su-2022-one-embedder-any-task-instruction/) — Hongjin Su, Weijia Shi, Jungo Kasai, et al. · 2022
- [RealTime QA: What's the Answer Right Now?](https://md.research.elamur.ai/free/en/papers/kasai-2022-realtime-qa-s-answer-right/) — Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, et al. · 2022
- [WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation](https://md.research.elamur.ai/free/en/papers/liu-2022-wanli-worker-ai-collaboration-natural/) — Alisa Liu, Swabha Swayamdipta, Noah A. Smith, et al. · 2022
- [A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers](https://md.research.elamur.ai/free/en/papers/dasigi-2021-dataset-information-seeking-questions-answers/) — Pradeep Dasigi, Kyle Lo, Iz Beltagy, et al. · 2021
- [DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts](https://md.research.elamur.ai/free/en/papers/liu-2021-dexperts-decoding-time-controlled-text/) — Alisa Liu, Maarten Sap, Ximing Lu, et al. · 2021
- [Measuring Association Between Labels and Free-Text Rationales](https://md.research.elamur.ai/free/en/papers/wiegreffe-2021-measuring-association-between-labels-free/) — Sarah Wiegreffe, Ana Marasović, Noah A. Smith · 2021
- [Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation](https://md.research.elamur.ai/free/en/papers/kasai-2020-deep-encoder-shallow-decoder-reevaluating/) — Jungo Kasai, Nikolaos Pappas, Hao Peng, et al. · 2020
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/) — Samuel Gehman, Suchin Gururangan, Maarten Sap, et al. · 2020
- [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://md.research.elamur.ai/free/en/papers/sap-2020-social-bias-frames-reasoning-about/) — Maarten Sap, Saadia Gabriel, Lianhui Qin, et al. · 2020
- [The Multilingual Amazon Reviews Corpus](https://md.research.elamur.ai/free/en/papers/keung-2020-multilingual-amazon-reviews-corpus/) — Phillip Keung, Yichao Lu, György Szarvas, et al. · 2020
- [Evaluating Gender Bias in Machine Translation](https://md.research.elamur.ai/free/en/papers/stanovsky-2019-evaluating-gender-bias-machine-translation/) — Gabriel Stanovsky, Noah A. Smith, Luke Zettlemoyer · 2019
- [Green AI](https://md.research.elamur.ai/free/en/papers/schwartz-2019-green-ai/) — Roy Schwartz, Jesse Dodge, Noah A. Smith, et al. · 2019
- [Is Attention Interpretable?](https://md.research.elamur.ai/free/en/papers/serrano-2019-is-attention-interpretable/) — Sofia Serrano, Noah A. Smith · 2019
- [Linguistic Knowledge and Transferability of Contextual Representations](https://md.research.elamur.ai/free/en/papers/liu-2019-linguistic-knowledge-transferability-contextual-representations/) — Nelson F. Liu, Matt Gardner, Yonatan Belinkov, et al. · 2019
- [The Risk of Racial Bias in Hate Speech Detection](https://md.research.elamur.ai/free/en/papers/sap-2019-risk-racial-bias-hate-speech/) — Maarten Sap, Dallas Card, Saadia Gabriel, et al. · 2019
- [Neural Cross-Lingual Named Entity Recognition with Minimal Resources](https://md.research.elamur.ai/free/en/papers/xie-2018-neural-cross-lingual-named-entity/) — Jiateng Xie, Zhilin Yang, Graham Neubig, et al. · 2018
- [Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold](https://md.research.elamur.ai/free/en/papers/swayamdipta-2017-frame-semantic-parsing-softmax-margin/) — Swabha Swayamdipta, Sam Thomson, Chris Dyer, et al. · 2017
- [Many Languages, One Parser](https://md.research.elamur.ai/free/en/papers/ammar-2016-many-languages-one-parser/) — Waleed Ammar, George Mulcaire, Miguel Ballesteros, et al. · 2016
- [Massively Multilingual Word Embeddings](https://md.research.elamur.ai/free/en/papers/ammar-2016-massively-multilingual-word-embeddings/) — Waleed Ammar, George Mulcaire, Yulia Tsvetkov, et al. · 2016
- [Frame-Semantic Role Labeling with Heterogeneous Annotations](https://md.research.elamur.ai/free/en/papers/kshirsagar-2015-frame-semantic-role-labeling-heterogeneous/) — Meghana Kshirsagar, Sam Thomson, Nathan Schneider, et al. · 2015
- [Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs](https://md.research.elamur.ai/free/en/papers/ballesteros-2015-improved-transition-based-parsing-modeling/) — Miguel Ballesteros, Chris Dyer, Noah A. Smith · 2015
- [The Media Frames Corpus: Annotations of Frames Across Issues](https://md.research.elamur.ai/free/en/papers/card-2015-media-frames-corpus-annotations-frames/) — Dallas Card, Amber E. Boydstun, Justin H. Gross, et al. · 2015
- [Toward Abstractive Summarization Using Semantic Representations](https://md.research.elamur.ai/free/en/papers/liu-2015-abstractive-summarization-semantic-representations/) — Fei Liu, Jeffrey Flanigan, Sam Thomson, et al. · 2015
- [A Bayesian Mixed Effects Model of Literary Character](https://md.research.elamur.ai/free/en/papers/bamman-2014-bayesian-mixed-effects-model-literary/) — David Bamman, Ted Underwood, Noah A. Smith · 2014
- [Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut](https://md.research.elamur.ai/free/en/papers/schneider-2014-discriminative-lexical-semantic-segmentation-gaps/) — Nathan Schneider, Emily Danchik, Chris Dyer, et al. · 2014
- [Frame-Semantic Parsing](https://md.research.elamur.ai/free/en/papers/das-2014-frame-semantic-parsing/) — Dipanjan Das, Desai Chen, André F. T. Martins, et al. · 2014
- [Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters](https://md.research.elamur.ai/free/en/papers/owoputi-2013-improved-part-speech-tagging-online/) — Olutobi Owoputi, Brendan O'Connor, Chris Dyer, et al. · 2013
- [Learning Latent Personas of Film Characters](https://md.research.elamur.ai/free/en/papers/bamman-2013-learning-latent-personas-film-characters/) — David Bamman, Brendan O'Connor, Noah A. Smith · 2013
- [Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments](https://md.research.elamur.ai/free/en/papers/gimpel-2011-part-speech-tagging-twitter-annotation/) — Kevin Gimpel, Nathan Schneider, Brendan O'Connor, et al. · 2011
- [Semi-Supervised Frame-Semantic Parsing for Unknown Predicates](https://md.research.elamur.ai/free/en/papers/das-2011-semi-supervised-frame-semantic-parsing/) — Dipanjan Das, Noah A. Smith · 2011
- [Probabilistic Frame-Semantic Parsing](https://md.research.elamur.ai/free/en/papers/das-2010-probabilistic-frame-semantic-parsing/) — Dipanjan Das, Nathan Schneider, Desai Chen, et al. · 2010
- [Paraphrase Identification as Probabilistic Quasi-Synchronous Recognition](https://md.research.elamur.ai/free/en/papers/das-2009-paraphrase-identification-as-probabilistic-quasi/) — Dipanjan Das, Noah A. Smith · 2009
- [What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA](https://md.research.elamur.ai/free/en/papers/wang-2007-what-is-jeopardy-model-quasi/) — Mengqiu Wang, Noah A. Smith, Teruko Mitamura · 2007
- [Contrastive Estimation: Training Log-Linear Models on Unlabeled Data](https://md.research.elamur.ai/free/en/papers/smith-2005-contrastive-estimation-training-log-linear/) — Noah A. Smith, Jason Eisner · 2005
- [The Web as a Parallel Corpus](https://md.research.elamur.ai/free/en/papers/resnik-2003-web-as-parallel-corpus/) — Philip Resnik, Noah A. Smith · 2003