M
Mark Hopkins
Researcher at Reed College
Publications - 35
Citations - 1566
Mark Hopkins is an academic researcher from Reed College. The author has contributed to research in topics: Machine translation & Parsing. The author has an hindex of 16, co-authored 33 publications receiving 1449 citations. Previous affiliations of Mark Hopkins include University of Potsdam & Allen Institute for Artificial Intelligence.
Papers
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What’s in a translation rule?
TL;DR: The theory is used to introduce a linear algorithm that can be used to derive from word-aligned, parallel corpora the minimal set of syntactically motivated transformation rules that explain human translation data.
Proceedings Article
Tuning as Ranking
Mark Hopkins,Jonathan May +1 more
TL;DR: Pro's scalability and effectiveness is established by comparing it to MERT and MIRA and parity is demonstrated on both phrase-based and syntax-based systems in a variety of language pairs, using large scale data scenarios.
Journal ArticleDOI
The Power of Extended Top-Down Tree Transducers
TL;DR: The obtained properties completely explain the Hasse diagram of the induced classes of tree transformations and it is shown that most interesting classes of transformations computed by extended top-down tree transducers are not closed under composition.
Proceedings ArticleDOI
Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples
TL;DR: The authors showed that recent advances in word representations greatly diminish the need for domain adaptation for parsers when the target domain is syntactically similar to the source domain, and they trained a parser on the Wall Street Journal alone that achieves over 90% F1 on the Brown corpus.
Patent
Training for a text-to-text application which uses string to tree conversion for training and decoding
TL;DR: In this paper, a target language is word aligned with a source language, and at least one of the languages is parsed into trees, which are used for training, by aligning conversion steps, forming a manual set of information representing the conversion steps and then learning rules from that reduced set.