Jessie Pinkham


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High quality machine translation using a machine-learned sentence realization component
Martine Smets | Michael Gamon | Jessie Pinkham | Tom Reutter | Martine Pettenaro
Proceedings of Machine Translation Summit IX: Papers

We describe the implementation of two new language pairs (English-French and English-German) which use machine-learned sentence realization components instead of hand-written generation components. The resulting systems are evaluated by human evaluators, and in the technical domain, are equal to the quality of highly respected commercial systems. We comment on the difficulties that are encountered when using machine-learned sentence realization in the context of MT.


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Machine Translation as a Testbed for Multilingual Analysis
Richard Campbell | Carmen Lozano | Jessie Pinkham | Martine Smets
COLING-02: Grammar Engineering and Evaluation

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MSR-MT: the Microsoft research machine translation system
Willaim B. Dolan | Jessie Pinkham | Stephen D. Richardson
Proceedings of the 5th Conference of the Association for Machine Translation in the Americas: System Descriptions

MSR-MT is an advanced research MT prototype that combines rule-based 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.

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Traduction automatique ancrée dans l’analyse linguistique
Jessie Pinkham | Martine Smets
Actes de la 9ème conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

Nous présentons dans cet article le système de traduction français-anglais MSR-MT développé à Microsoft dans le groupe de recherche sur le traitement du language (NLP). Ce système est basé sur des analyseurs sophistiqués qui produisent des formes logiques, dans la langue source et la langue cible. Ces formes logiques sont alignées pour produire la base de données du transfert, qui contient les correspondances entre langue source et langue cible, utilisées lors de la traduction. Nous présentons différents stages du développement de notre système, commencé en novembre 2000. Nous montrons que les performances d’octobre 2001 de notre système sont meilleures que celles du système commercial Systran, pour le domaine technique, et décrivons le travail linguistique qui nous a permis d’arriver à cette performance. Nous présentons enfin les résultats préliminaires sur un corpus plus général, les débats parlementaires du corpus du Hansard. Quoique nos résultats ne soient pas aussi concluants que pour le domaine technique, nous sommes convaincues que la résolution des problèmes d’analyse que nous avons identifiés nous permettra d’améliorer notre performance.

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Modular MT with a Learned Bilingual Dictionary: Rapid Deployment of a New Language Pair
Jessie Pinkham | Martine Smets
COLING 2002: The 19th International Conference on Computational Linguistics

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Machine translation without a bilingual dictionary
Jessie Pinkham | Martine Smets
Proceedings of the 9th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages: Papers


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Rapid assembly of a large-scale French-English MT system
Jessie Pinkham | Monica Corston-Oliver | Martine Smets | Martine Pettenaro
Proceedings of Machine Translation Summit VIII

Past research has shown that the ideal MT system should be modular and devoid of language pair specific information in its design. We describe here the assembly of TAMTAM (Traduction Automatique Microsoft), the French-English research MT system under development at Microsoft, which was constructed from a combination of pre-existing rule-based components and automatically created components. At this stage, the system has not been adapted either computationally or linguistically to the French-English context and yet it performs only slightly below the French-English Systran system in independent blind human evaluations

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Achieving commercial-quality translation with example-based methods
Stephen Richardson | William Dolan | Arul Menezes | Jessie Pinkham
Proceedings of Machine Translation Summit VIII

We describe MSR-MT, a large-scale example-based machine translation system under development for several language pairs. Trained on aligned English-Spanish technical prose, a blind evaluation shows that MSR-MT’s integration of rule-based parsers, example based processing, and statistical techniques produces translations whose quality in this domain exceeds that of uncustomized commercial MT systems.

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Adding Domain Specificity to an MT System
Jessie Pinkham | Monica Corston-Oliver
Proceedings of the ACL 2001 Workshop on Data-Driven Methods in Machine Translation


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Tools for Large-Scale Parser Development
Hisami Suzuki | Jessie Pinkham
Proceedings of the COLING-2000 Workshop on Efficiency In Large-Scale Parsing Systems


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Practical Experience with Grammar Sharing in Multilingual NLP
Michael Gamon | Carmen Lozano | Jessie Pinkham | Tom Reutter
From Research to Commercial Applications: Making NLP Work in Practice