Ann Irvine


2017

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A Comprehensive Analysis of Bilingual Lexicon Induction
Ann Irvine | Chris Callison-Burch
Computational Linguistics, Volume 43, Issue 2 - June 2017

Bilingual lexicon induction is the task of inducing word translations from monolingual corpora in two languages. In this article we present the most comprehensive analysis of bilingual lexicon induction to date. We present experiments on a wide range of languages and data sizes. We examine translation into English from 25 foreign languages: Albanian, Azeri, Bengali, Bosnian, Bulgarian, Cebuano, Gujarati, Hindi, Hungarian, Indonesian, Latvian, Nepali, Romanian, Serbian, Slovak, Somali, Spanish, Swedish, Tamil, Telugu, Turkish, Ukrainian, Uzbek, Vietnamese, and Welsh. We analyze the behavior of bilingual lexicon induction on low-frequency words, rather than testing solely on high-frequency words, as previous research has done. Low-frequency words are more relevant to statistical machine translation, where systems typically lack translations of rare words that fall outside of their training data. We systematically explore a wide range of features and phenomena that affect the quality of the translations discovered by bilingual lexicon induction. We provide illustrative examples of the highest ranking translations for orthogonal signals of translation equivalence like contextual similarity and temporal similarity. We analyze the effects of frequency and burstiness, and the sizes of the seed bilingual dictionaries and the monolingual training corpora. Additionally, we introduce a novel discriminative approach to bilingual lexicon induction. Our discriminative model is capable of combining a wide variety of features that individually provide only weak indications of translation equivalence. When feature weights are discriminatively set, these signals produce dramatically higher translation quality than previous approaches that combined signals in an unsupervised fashion (e.g., using minimum reciprocal rank). We also directly compare our model’s performance against a sophisticated generative approach, the matching canonical correlation analysis (MCCA) algorithm used by Haghighi et al. (2008). Our algorithm achieves an accuracy of 42% versus MCCA’s 15%.

2014

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Hallucinating Phrase Translations for Low Resource MT
Ann Irvine | Chris Callison-Burch
Proceedings of the Eighteenth Conference on Computational Natural Language Learning

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Using Comparable Corpora to Adapt MT Models to New Domains
Ann Irvine | Chris Callison-Burch
Proceedings of the Ninth Workshop on Statistical Machine Translation

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The Language Demographics of Amazon Mechanical Turk
Ellie Pavlick | Matt Post | Ann Irvine | Dmitry Kachaev | Chris Callison-Burch
Transactions of the Association for Computational Linguistics, Volume 2

We present a large scale study of the languages spoken by bilingual workers on Mechanical Turk (MTurk). We establish a methodology for determining the language skills of anonymous crowd workers that is more robust than simple surveying. We validate workers’ self-reported language skill claims by measuring their ability to correctly translate words, and by geolocating workers to see if they reside in countries where the languages are likely to be spoken. Rather than posting a one-off survey, we posted paid tasks consisting of 1,000 assignments to translate a total of 10,000 words in each of 100 languages. Our study ran for several months, and was highly visible on the MTurk crowdsourcing platform, increasing the chances that bilingual workers would complete it. Our study was useful both to create bilingual dictionaries and to act as census of the bilingual speakers on MTurk. We use this data to recommend languages with the largest speaker populations as good candidates for other researchers who want to develop crowdsourced, multilingual technologies. To further demonstrate the value of creating data via crowdsourcing, we hire workers to create bilingual parallel corpora in six Indian languages, and use them to train statistical machine translation systems.

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The American Local News Corpus
Ann Irvine | Joshua Langfus | Chris Callison-Burch
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present the American Local News Corpus (ALNC), containing over 4 billion words of text from 2,652 online newspapers in the United States. Each article in the corpus is associated with a timestamp, state, and city. All 50 U.S. states and 1,924 cities are represented. We detail our method for taking daily snapshots of thousands of local and national newspapers and present two example corpus analyses. The first explores how different sports are talked about over time and geography. The second compares per capita murder rates with news coverage of murders across the 50 states. The ALNC is about the same size as the Gigaword corpus and is growing continuously. Version 1.0 is available for research use.

2013

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Monolingual Marginal Matching for Translation Model Adaptation
Ann Irvine | Chris Quirk | Hal Daumé III
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Combining Bilingual and Comparable Corpora for Low Resource Machine Translation
Ann Irvine | Chris Callison-Burch
Proceedings of the Eighth Workshop on Statistical Machine Translation

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The (Un)faithful Machine Translator
Ruth Jones | Ann Irvine
Proceedings of the 7th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities

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Measuring Machine Translation Errors in New Domains
Ann Irvine | John Morgan | Marine Carpuat | Hal Daumé III | Dragos Munteanu
Transactions of the Association for Computational Linguistics, Volume 1

We develop two techniques for analyzing the effect of porting a machine translation system to a new domain. One is a macro-level analysis that measures how domain shift affects corpus-level evaluation; the second is a micro-level analysis for word-level errors. We apply these methods to understand what happens when a Parliament-trained phrase-based machine translation system is applied in four very different domains: news, medical texts, scientific articles and movie subtitles. We present quantitative and qualitative experiments that highlight opportunities for future research in domain adaptation for machine translation.

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SenseSpotting: Never let your parallel data tie you to an old domain
Marine Carpuat | Hal Daumé III | Katharine Henry | Ann Irvine | Jagadeesh Jagarlamudi | Rachel Rudinger
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Supervised Bilingual Lexicon Induction with Multiple Monolingual Signals
Ann Irvine | Chris Callison-Burch
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Statistical Machine Translation in Low Resource Settings
Ann Irvine
Proceedings of the 2013 NAACL HLT Student Research Workshop

2012

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Processing Informal, Romanized Pakistani Text Messages
Ann Irvine | Jonathan Weese | Chris Callison-Burch
Proceedings of the Second Workshop on Language in Social Media

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Digitizing 18th-Century French Literature: Comparing transcription methods for a critical edition text
Ann Irvine | Laure Marcellesi | Afra Zomorodian
Proceedings of the NAACL-HLT 2012 Workshop on Computational Linguistics for Literature

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Proceedings of ACL 2012 Student Research Workshop
Jackie C. K. Cheung | Jun Hatori | Carlos Henriquez | Ann Irvine
Proceedings of ACL 2012 Student Research Workshop

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Toward Statistical Machine Translation without Parallel Corpora
Alexandre Klementiev | Ann Irvine | Chris Callison-Burch | David Yarowsky
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

2011

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Feature-Rich Language-Independent Syntax-Based Alignment for Statistical Machine Translation
Jason Riesa | Ann Irvine | Daniel Marcu
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

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Using Mechanical Turk to Annotate Lexicons for Less Commonly Used Languages
Ann Irvine | Alexandre Klementiev
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk

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Joshua 2.0: A Toolkit for Parsing-Based Machine Translation with Syntax, Semirings, Discriminative Training and Other Goodies
Zhifei Li | Chris Callison-Burch | Chris Dyer | Juri Ganitkevitch | Ann Irvine | Sanjeev Khudanpur | Lane Schwartz | Wren Thornton | Ziyuan Wang | Jonathan Weese | Omar Zaidan
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR