Corey Miller


2020

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Plugging into Trados: Augmenting Translation in the Enclave
Corey Miller | Chiara Higgins | Paige Havens | Steven Van Guilder | Rodney Morris | Danielle Silverman
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)

2018

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Embedding Register-Aware MT into the CAT Workflow
Corey Miller | Danielle Silverman | Vanesa Jurica | Elizabeth Richerson | Rodney Morris | Elisabeth Mallard
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)

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Evaluating Automatic Speech Recognition in Translation
Evelyne Tzoukermann | Corey Miller
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)

2014

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Employing Phonetic Speech Recognition for Language and Dialect Specific Search
Corey Miller | Rachel Strong | Evan Jones | Mark Vinson
Proceedings of the First Workshop on Applying NLP Tools to Similar Languages, Varieties and Dialects

2010

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Error Correction for Arabic Dictionary Lookup
C. Anton Rytting | Paul Rodrigues | Tim Buckwalter | David Zajic | Bridget Hirsch | Jeff Carnes | Nathanael Lynn | Sarah Wayland | Chris Taylor | Jason White | Charles Blake III | Evelyn Browne | Corey Miller | Tristan Purvis
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We describe a new Arabic spelling correction system which is intended for use with electronic dictionary search by learners of Arabic. Unlike other spelling correction systems, this system does not depend on a corpus of attested student errors but on student- and teacher-generated ratings of confusable pairs of phonemes or letters. Separate error modules for keyboard mistypings, phonetic confusions, and dialectal confusions are combined to create a weighted finite-state transducer that calculates the likelihood that an input string could correspond to each citation form in a dictionary of Iraqi Arabic. Results are ranked by the estimated likelihood that a citation form could be misheard, mistyped, or mistranscribed for the input given by the user. To evaluate the system, we developed a noisy-channel model trained on studentsÂ’ speech errors and use it to perturb citation forms from a dictionary. We compare our system to a baseline based on Levenshtein distance and find that, when evaluated on single-error queries, our system performs 28% better than the baseline (overall MRR) and is twice as good at returning the correct dictionary form as the top-ranked result. We believe this to be the first spelling correction system designed for a spoken, colloquial dialect of Arabic.