[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/authors/regina-barzilay/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/) / Regina Barzilay # Regina Barzilay 7 paper cards in Mova's library · 2001–2021 Regina Barzilay wrote 7 of the papers Mova reads, in 2001–2021, most of them on [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/), [Akkadian and cuneiform NLP](https://md.mova.elamur.ai/free/en/docs/papers/topics/akkadian-cuneiform-nlp/), [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/); often with [Jiaming Luo](https://md.mova.elamur.ai/free/en/docs/papers/authors/jiaming-luo/), [Tao Lei](https://md.mova.elamur.ai/free/en/docs/papers/authors/tao-lei/), [Yuan Cao](https://md.mova.elamur.ai/free/en/docs/papers/authors/yuan-cao/). [OpenAlex](https://openalex.org/A5010124873) · [ORCID](https://orcid.org/0000-0002-2921-8201) · [**h-index 93**](https://openalex.org/authors/A5010124873) · [36278 citations](https://openalex.org/authors/A5010124873) · [422 works](https://openalex.org/works?filter=author.id:A5010124873) — 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=Regina+Barzilay) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Regina+Barzilay) · [dblp](https://dblp.org/search?q=Regina+Barzilay) · [ACL Anthology](https://aclanthology.org/search/?q=Regina+Barzilay) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Regina+Barzilay) Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 4 · [Akkadian and cuneiform NLP](https://md.mova.elamur.ai/free/en/docs/papers/topics/akkadian-cuneiform-nlp/) 1 · [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/) 1 ## Ideas Mova took (1) From the ideas ledger: an idea tried, kept or queued, or measured with no gain, and what it gave. - **tried** — Align all English renderings of one verse across the translations in the same way. The shared backbone is the frame-preserving part of the verse, and the slots are the author delta. A slot filled in three or more translations goes to the standard; a slot… *([Learning to Paraphrase: An Unsupervised Approach Using…](https://md.mova.elamur.ai/free/en/docs/papers/barzilay-2003-learning-paraphrase-unsupervised-approach-multiple/) · translate r6-lattice: the four Bibles' renderings of each training verse aligned (LCS of concepts); the standard learned on the shared backbone only (1.20 M…)* ## Papers 1. **[Deciphering Undersegmented Ancient Scripts Using Phonetic Prior](https://md.mova.elamur.ai/free/en/docs/papers/tacl-2021-luo-deciphering-undersegmented-ancient-scripts-phonetic/)** *(2021)* The paper asks how to match an unsegmented or undersegmented script against the vocabulary of a known language when the language family of the lost script is not known. 2. **[Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B](https://md.mova.elamur.ai/free/en/docs/papers/acl-2019-luo-neural-decipherment-minimum-cost-flow/)** *(2019)* The paper deciphers a lost language without parallel text: the words of the lost language are aligned with cognates in a known, related language. 3. **[Style Transfer from Non-Parallel Text by Cross-Alignment](https://md.mova.elamur.ai/free/en/docs/papers/shen-2017-style-transfer-non-parallel-text/)** *(2017)* The paper transfers style between two corpora that share one content distribution but no sentence pairs. An encoder maps a sentence, together with its style label, to a style-free latent content vector; a style-conditioned decoder renders it back. Training aligns populations rather than pairs: sentences transferred… 4. **[Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment](https://md.mova.elamur.ai/free/en/docs/papers/tacl-2014-belinkov-exploring-compositional-architectures-word-vector/)** *(2014)* PP attachment chooses which candidate head a prepositional phrase depends on. The paper scores each candidate by composing the word vectors of the head, the preposition and the child in a small neural network, trained on attachment decisions. The English data are the Penn Treebank converted to dependencies (sections… 5. **[Unsupervised Morphology Rivals Supervised Morphology for Arabic MT](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2012-stallard-unsupervised-morphology-rivals-supervised-morphology/)** *(2012)* The question is whether an unsupervised Arabic segmenter (the Lee et al. Bayesian model) can match supervised segmenters such as MADA and Sakhr when it feeds a hierarchical string-to-tree MT system. The authors add maximum marginal decoding (MM) over 25 Gibbs-sampling runs. Sampling alone changes 13% of token… 6. **[Learning to Paraphrase: An Unsupervised Approach Using Multiple-Sequence Alignment](https://md.mova.elamur.ai/free/en/docs/papers/barzilay-2003-learning-paraphrase-unsupervised-approach-multiple/)** *(2003)* The paper addresses sentence-level paraphrase, which is harder than word or phrase paraphrase, using comparable news corpora: different sources reporting the same events. 7. **[Extracting Paraphrases from a Parallel Corpus](https://md.mova.elamur.ai/free/en/docs/papers/barzilay-2001-extracting-paraphrases-parallel-corpus/)** *(2001)* The paper extracts paraphrases from several English translations of the same foreign source text, a comparable corpus that differs in wording but not in meaning. ## Co-authors (15) - [Jiaming Luo](https://md.mova.elamur.ai/free/en/docs/papers/authors/jiaming-luo/) ×2 - [Tao Lei](https://md.mova.elamur.ai/free/en/docs/papers/authors/tao-lei/) ×2 - [Yuan Cao](https://md.mova.elamur.ai/free/en/docs/papers/authors/yuan-cao/) ×2 - Amir Globerson - David Stallard - [Enrico Santus](https://md.mova.elamur.ai/free/en/docs/papers/authors/enrico-santus/) - Frederik Hartmann - Jacob Devlin - Kathleen R. McKeown - [Lillian Lee](https://md.mova.elamur.ai/free/en/docs/papers/authors/lillian-lee/) - Michael Kayser - Tianxiao Shen - Tommi Jaakkola - [Yonatan Belinkov](https://md.mova.elamur.ai/free/en/docs/papers/authors/yonatan-belinkov/) - Yoong Keok Lee - [← previous Refael Shaked Greenfeld](https://md.mova.elamur.ai/free/en/docs/papers/authors/refael-shaked-greenfeld/) - [next → Reham Marzouk](https://md.mova.elamur.ai/free/en/docs/papers/authors/reham-marzouk/) - [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)