[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/authors/felipe-sanchez-martinez/) [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/) / Felipe Sánchez-Martínez # Felipe Sánchez-Martínez 5 paper cards in Mova's library · 2007–2020 Felipe Sánchez-Martínez wrote 5 of the papers Mova reads, in 2007–2020, most of them on [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/); often with [Mikel L. Forcada](https://md.mova.elamur.ai/free/en/docs/papers/authors/mikel-l-forcada/), [Juan Antonio Perez-Ortiz](https://md.mova.elamur.ai/free/en/docs/papers/authors/juan-antonio-perez-ortiz/), [Francis M. Tyers](https://md.mova.elamur.ai/free/en/docs/papers/authors/francis-m-tyers/). Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Felipe+S%C3%A1nchez-Mart%C3%ADnez) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Felipe+S%C3%A1nchez-Mart%C3%ADnez) · [dblp](https://dblp.org/search?q=Felipe+S%C3%A1nchez-Mart%C3%ADnez) · [ACL Anthology](https://aclanthology.org/search/?q=Felipe+S%C3%A1nchez-Mart%C3%ADnez) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Felipe+S%C3%A1nchez-Mart%C3%ADnez) Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 5 ## Ideas Mova took (1) From the ideas ledger: an idea tried, kept or queued, or measured with no gain, and what it gave. - **tried** — A count test for lexicon merges. Merge two concepts (or two meta tokens) only when their proportions over the same contexts agree under a dissimilarity like the paper's, computed from counts. This targets open problem 2. Measure the merges kept, including… *([Learning synchronous context-free grammars with multiple…](https://md.mova.elamur.ai/free/en/docs/papers/sanchez-2020-martinez-learning-synchronous-context-free-grammars/) · translate r6-lb: a count/lower-bound test for merges (with riding-2008 I1); kept)* ## Papers 1. **[Learning synchronous context-free grammars with multiple specialised non-terminals for hierarchical phrase-based translation](https://md.mova.elamur.ai/free/en/docs/papers/sanchez-2020-martinez-learning-synchronous-context-free-grammars/)** *(2020)* Hierarchical phrase-based translation uses one nonterminal, which the authors take to be overloaded. They give every gap of every extracted phrase pair its own nonterminal, which yields about 850,000 nonterminals on a small English-Spanish EMEA corpus (sentences up to 20 words). Two nonterminals are then merged when… 2. **[Flexible finite-state lexical selection for rule-based machine translation](https://md.mova.elamur.ai/free/en/docs/papers/tyers-2012-flexible-finite-state-lexical-selection/)** *(2012)* The paper adds a lexical-selection module to the Apertium pipeline. When a dictionary offers several target words for one source word, a rule chooses one from the context. Rules are written in XML as context patterns, compiled into a finite-state transducer, and applied with a best-coverage dynamic program that… 3. **[Integrating shallow-transfer rules into phrase-based statistical machine translation](https://md.mova.elamur.ai/free/en/docs/papers/cartagena-2011-integrating-shallow-transfer-rules-into/)** *(2011)* A hybrid method that feeds a phrase-based statistical system with the knowledge of a shallow-transfer rule system. 4. **[Inferring Shallow-Transfer Machine Translation Rules from Small Parallel Corpora](https://md.mova.elamur.ai/free/en/docs/papers/sanchez-2014-martinez-inferring-shallow-transfer-machine-translation/)** *(2009)* A longer and fuller version of the 2007 method. Shallow-transfer rules are inferred from sentence-aligned corpora that are small compared with those used for statistical MT. The method is unsupervised. It uses alignment templates, extended with restrictions taken from the dictionary of the MT system so that each… 5. **[Automatic induction of shallow-transfer rules for open-source machine translation](https://md.mova.elamur.ai/free/en/docs/papers/martinez-2007-automatic-induction-shallow-transfer-rules/)** *(2007)* The method learns the structural transfer rules of a shallow-transfer MT system from a small parallel corpus, with no supervision. ## Co-authors (5) - [Mikel L. Forcada](https://md.mova.elamur.ai/free/en/docs/papers/authors/mikel-l-forcada/) ×3 - [Juan Antonio Perez-Ortiz](https://md.mova.elamur.ai/free/en/docs/papers/authors/juan-antonio-perez-ortiz/) ×2 - [Francis M. Tyers](https://md.mova.elamur.ai/free/en/docs/papers/authors/francis-m-tyers/) - Rafael C. Carrasco - Víctor M. Sánchez-Cartagena - [← previous Felice Dell'Orletta](https://md.mova.elamur.ai/free/en/docs/papers/authors/felice-dell-orletta/) - [next → Fernando Pereira](https://md.mova.elamur.ai/free/en/docs/papers/authors/fernando-pereira/) - [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)