Amittai Axelrod

Application of statistical machine translation to public health information: a feasibility study

OBJECTIVE Accurate, understandable public health information is important for ensuring the health of the nation. The large portion of the US population with Limited English Proficiency is best served by translations of public-health information into other languages. However, a large number of health departments and primary care clinics face significant barriers to fulfilling federal mandates to provide multilingual materials to Limited English Proficiency individuals. This article presents a pilot study on the feasibility of using freely available statistical machine translation technology to translate health promotion materials. DESIGN The authors gathered health-promotion materials in English from local and national public-health websites. Spanish versions were created by translating the documents using a freely available machine-translation website. Translations were rated for adequacy and fluency, analyzed for errors, manually corrected by a human posteditor, and compared with exclusively manual translations. RESULTS Machine translation plus postediting took 15-53 min per document, compared to the reported days or even weeks for the standard translation process. A blind comparison of machine-assisted and human translations of six documents revealed overall equivalency between machine-translated and manually translated materials. The analysis of translation errors indicated that the most important errors were word-sense errors. CONCLUSION The results indicate that machine translation plus postediting may be an effective method of producing multilingual health materials with equivalent quality but lower cost compared to manual translations.

Data Selection With Fewer Words

We present a method that improves data selection by combining a hybrid word/part-of-speech representation for corpora, with the idea of distinguishing between rare and frequent events. We validate our approach using data selection for machine translation, and show that it maintains or improves BLEU and TER translation scores while substantially improving vocabulary coverage and reducing data selection model size. Paradoxically, the coverage improvement is achieved by abstracting away over 97% of the total training corpus vocabulary using simple part-of-speech tags during the data selection process.

New methods and evaluation experiments on translating TED talks in the IWSLT benchmark

The IWSLT benchmark task is an annual evaluation campaign on spoken language translation held by the International Workshop on Spoken Language Processing (IWSLT). The task is to translate TED talks (www.ted.com). This task presents two unique challenges: Firstly, the underlying topic switches sharply from talk to talk, and each one contains only tens to hundreds of utterances. The translation system therefore needs to adapt to the current topic quickly and dynamically. Secondly, unlike other machine translation benchmark tasks, only a very small relevant parallel corpus (transcripts of TED talks) is available. Therefore, it is necessary to perform accurate translation model estimation with limited data. In this paper, we present our recent progress and two new methods on the IWSLT TED talk translation task from Chinese into English. In particular, to address the first problem, we use unsupervised topic modeling to select additional topic-dependent parallel data from a globally irrelevant corpus. These additional data slices can then be used to build an unsupervised topic-adapted machine translation system. For the second problem, we develop a discriminative training method to estimate the translation models more accurately. Our experimental evaluation results show that both methods improve the translation quality over a state-of-the-art baseline.

The University of Washington Machine Translation System for ACL WMT 2008

This paper present the University of Washingtons submission to the 2008 ACL SMT shared machine translation task. Two systems, for English-to-Spanish and German-to-Spanish translation are described. Our main focus was on testing a novel boosting framework for N-best list reranking and on handling German morphology in the German-to-Spanish system. While boosted N-best list reranking did not yield any improvements for this task, simplifying German morphology as part of the preprocessing step did result in significant gains.