William Lewis

Also published as: William D. Lewis


2015

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Skype Translator: Breaking down language and hearing barriers. A behind the scenes look at near real-time speech translation
William Lewis
Proceedings of Translating and the Computer 37

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Applying cross-entropy difference for selecting parallel training data from publicly available sources for conversational machine translation
William Lewis | Christian Federmann | Ying Xin
Proceedings of the 12th International Workshop on Spoken Language Translation: Papers

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Enriching Interlinear Text using Automatically Constructed Annotators
Ryan Georgi | Fei Xia | William Lewis
Proceedings of the 9th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH)

2014

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Enriching ODIN
Fei Xia | William Lewis | Michael Wayne Goodman | Joshua Crowgey | Emily M. Bender
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper, we describe the expansion of the ODIN resource, a database containing many thousands of instances of Interlinear Glossed Text (IGT) for over a thousand languages harvested from scholarly linguistic papers posted to the Web. A database containing a large number of instances of IGT, which are effectively richly annotated and heuristically aligned bitexts, provides a unique resource for bootstrapping NLP tools for resource-poor languages. To make the data in ODIN more readily consumable by tool developers and NLP researchers, we propose a new XML format for IGT, called Xigt. We call the updated release ODIN-II.

2013

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Enhanced and Portable Dependency Projection Algorithms Using Interlinear Glossed Text
Ryan Georgi | Fei Xia | William D. Lewis
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Dramatically Reducing Training Data Size Through Vocabulary Saturation
William Lewis | Sauleh Eetemadi
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Controlled Ascent: Imbuing Statistical MT with Linguistic Knowledge
William Lewis | Chris Quirk
Proceedings of the Second Workshop on Hybrid Approaches to Translation

2012

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Building MT for a Severely Under-Resourced Language: White Hmong
William Lewis | Phong Yang
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers

In this paper, we discuss the development of statistical machine translation for English to/from White Hmong (Language code: mww). White Hmong is a Hmong-Mien language, originally spoken mostly in Southeast Asia, but now predominantly spoken by a large diaspora throughout the world, with populations in the United States, Australia, France, Thailand and elsewhere. Building statistical translation systems for Hmong proved to be incredibly challenging since there are no known parallel or monolingual corpora for the language; in fact, finding data for Hmong proved to be one of the biggest challenges to getting the project off the ground. It was only through a close collaboration with the Hmong community, and active and tireless participation of Hmong speakers, that it became possible to build up a critical mass of data to make the translation project a reality. We see this effort as potentially replicable for other severely resource poor languages of the world, which is likely the case for the majority of the languages still spoken on the planet. Further, the work here suggests that research and work on other severely under-resourced languages can have significant positive impacts for the affected communities, both for accessibility and language preservation.

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Measuring the Divergence of Dependency Structures Cross-Linguistically to Improve Syntactic Projection Algorithms
Ryan Georgi | Fei Xia | William Lewis
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Syntactic parses can provide valuable information for many NLP tasks, such as machine translation, semantic analysis, etc. However, most of the world's languages do not have large amounts of syntactically annotated corpora available for building parsers. Syntactic projection techniques attempt to address this issue by using parallel corpora between resource-poor and resource-rich languages, bootstrapping the resource-poor language with the syntactic analysis of the resource-rich language. In this paper, we investigate the possibility of using small, parallel, annotated corpora to automatically detect divergent structural patterns between two languages. These patterns can then be used to improve structural projection algorithms, allowing for better performing NLP tools for resource-poor languages, in particular those that may not have large amounts of annotated data necessary for traditional, fully-supervised methods. While this detection process is not exhaustive, we demonstrate that important instances of divergence are picked up with minimal prior knowledge of a given language pair.

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Applications of data selection via cross-entropy difference for real-world statistical machine translation
Amittai Axelrod | QingJun Li | William D. Lewis
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

We broaden the application of data selection methods for domain adaptation to a larger number of languages, data, and decoders than shown in previous work, and explore comparable applications for both monolingual and bilingual cross-entropy difference methods. We compare domain adapted systems against very large general-purpose systems for the same languages, and do so without a bias to a particular direction. We present results against real-world generalpurpose systems tuned on domain-specific data, which are substantially harder to beat than standard research baseline systems. We show better performance for nearly all domain adapted systems, despite the fact that the domainadapted systems are trained on a fraction of the content of their general domain counterparts. The high performance of these methods suggest applicability to a wide variety of contexts, particularly in scenarios where only small supplies of unambiguously domain-specific data are available, yet it is believed that additional similar data is included in larger heterogenous-content general-domain corpora.

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Improving Dependency Parsing with Interlinear Glossed Text and Syntactic Projection
Ryan Georgi | Fei Xia | William Lewis
Proceedings of COLING 2012: Posters

2011

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Crisis MT: Developing A Cookbook for MT in Crisis Situations
William Lewis | Robert Munro | Stephan Vogel
Proceedings of the Sixth Workshop on Statistical Machine Translation

2010

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Haitian Creole: How to Build and Ship an MT Engine from Scratch in 4 days, 17 hours, & 30 minutes
William Lewis
Proceedings of the 14th Annual conference of the European Association for Machine Translation

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Achieving Domain Specificity in SMT without Overt Siloing
William D. Lewis | Chris Wendt | David Bullock
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We examine pooling data as a method for improving Statistical Machine Translation (SMT) quality for narrowly defined domains, such as data for a particular company or public entity. By pooling all available data, building large SMT engines, and using domain-specific target language models, we see boosts in quality, and can achieve the generalizability and resiliency of a larger SMT but with the precision of a domain-specific engine.

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The Problems of Language Identification within Hugely Multilingual Data Sets
Fei Xia | Carrie Lewis | William D. Lewis
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

As the data for more and more languages is finding its way into digital form, with an increasing amount of this data being posted to the Web, it has become possible to collect language data from the Web and create large multilingual resources, covering hundreds or even thousands of languages. ODIN, the Online Database of INterlinear text (Lewis, 2006), is such a resource. It currently consists of nearly 200,000 data points for over 1,000 languages, the data for which was harvested from linguistic documents on the Web. We identify a number of issues with language identification for such broad-coverage resources including the lack of training data, ambiguous language names, incomplete language code sets, and incorrect uses of language names and codes. After providing a short overview of existing language code sets maintained by the linguistic community, we discuss what linguists and the linguistic community can do to make the process of language identification easier.

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Intelligent Selection of Language Model Training Data
Robert C. Moore | William Lewis
Proceedings of the ACL 2010 Conference Short Papers

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Comparing Language Similarity across Genetic and Typologically-Based Groupings
Ryan Georgi | Fei Xia | William Lewis
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Proceedings of the 2010 Workshop on NLP and Linguistics: Finding the Common Ground
Fei Xia | William Lewis | Lori Levin
Proceedings of the 2010 Workshop on NLP and Linguistics: Finding the Common Ground

2009

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Language ID in the Context of Harvesting Language Data off the Web
Fei Xia | William Lewis | Hoifung Poon
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Parsing, Projecting & Prototypes: Repurposing Linguistic Data on the Web
William Lewis | Fei Xia
Proceedings of the Demonstrations Session at EACL 2009

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Applying NLP Technologies to the Collection and Enrichment of Language Data on the Web to Aid Linguistic Research
Fei Xia | William Lewis
Proceedings of the EACL 2009 Workshop on Language Technology and Resources for Cultural Heritage, Social Sciences, Humanities, and Education (LaTeCH – SHELT&R 2009)

2008

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Repurposing Theoretical Linguistic Data for Tool Development and Search
Fei Xia | William D. Lewis
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

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Automatically Identifying Computationally Relevant Typological Features
William D. Lewis | Fei Xia
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II

2007

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Multilingual Structural Projection across Interlinear Text
Fei Xia | William Lewis
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

2004

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The Semantics of Markup: Mapping Legacy Markup Schemas to a Common Semantics
Gary Simons | William Lewis | Scott Farrar | Terence Langendoen | Brian Fitzsimons | Hector Gonzalez
Proceeedings of the Workshop on NLP and XML (NLPXML-2004): RDF/RDFS and OWL in Language Technology