Stephen D. Richardson

MindNet: Acquiring and Structuring Semantic Information from Text

As a lexical knowledge base constructed automatically from the definitions and example sentences in two machine-readable dictionaries (MRDs), MindNet embodies several features that distinguish it from prior work with MRDs. It is, however, more than this static resource alone. MindNet represents a general methodology for acquiring, structuring, accessing, and exploiting semantic information from natural language text. This paper provides an overview of the distinguishing characteristics of MindNet, the steps involved in its creation, and its extension beyond dictionary text.

A best-first alignment algorithm for automatic extraction of transfer mappings from bilingual corpora

Translation systems that automatically extract transfer mappings (rules or examples) from bilingual corpora have been hampered by the difficulty of achieving accurate alignment and acquiring high quality mappings. We describe an algorithm that uses a best-first strategy and a small alignment grammar to significantly improve the quality of the transfer mappings extracted. For each mapping, frequencies are computed and sufficient context is retained to distinguish competing mappings during translation. Variants of the algorithm are run against a corpus containing 200K sentence pairs and evaluated based on the quality of resulting translations.

Overcoming the customization bottleneck using example-based MT

We describe MSR-MT, a large-scale hybrid machine translation system under development for several language pairs. This systems ability to acquire its primary translation knowledge automatically by parsing a bilingual corpus of hundreds of thousands of sentence pairs and aligning resulting logical forms demonstrates true promise for overcoming the so-called MT customization bottleneck. Trained on English and Spanish technical prose, a blind evaluation shows that MSR-MTs integration of rule-based parsers, example based processing, and statistical techniques produces translations whose quality exceeds that of uncustomized commercial MT systems in this domain.

MSR-MT: The Microsoft Research Machine Translation System

MSR-MT is an advanced research MT prototype that combines rulebased and statistical techniques with example-based transfer. This hybrid, large-scale system is capable of learning all its knowledge of lexical and phrasal translations directly from data. MSR-MT has undergone rigorous evaluation showing that, trained on a corpus of technical data similar to the test corpus, its output surpasses the quality of best-of-breed commercial MT systems.

Machine translation of online Product Support articles using a data-driven MT system

At AMTA 2002, we reported on a pilot project to machine translate Microsoft’s Product Support Knowledge Base into Spanish. The successful pilot has since resulted in the permanent deployment of both Spanish and Japanese versions of the knowledge base, as well as ongoing pilot projects for French and German. The translated articles in each case have been produced by MSR-MT, Microsoft Research’s data-driven MT system, which has been trained on well over a million bilingual sentence pairs for each target language from previously translated materials contained in translation memories and glossaries. This paper describes our experience in deploying this system and the (positive) customer response to the availability of machine translated articles, as well as other uses of MSR-MT either planned or underway at Microsoft.