David Svoboda


Diagnostics for interactive controlled language checking
Teruko Mitamura | Kathryn Baker | Eric Nyberg | David Svoboda
EAMT Workshop: Improving MT through other language technology tools: resources and tools for building MT

Source language diagnostics for MT
Teruko Mitamura | Kathryn Baker | David Svoboda | Eric Nyberg
Proceedings of Machine Translation Summit IX: Papers

This paper presents a source language diagnostic system for controlled translation. Diagnostics were designed and implemented to address the most difficult rewrites for authors, based on an empirical analysis of log files containing over 180,000 sentences. The design and implementation of the diagnostic system are presented, along with experimental results from an empirical evaluation of the completed system. We found that the diagnostic system can correctly identify the problem in 90.2% of the cases. In addition, depending on the type of grammar problem, the diagnostic system may offer a rewritten sentence. We found that 89.4% of the rewritten sentences were correctly rewritten. The results suggest that these methods could be used as the basis for an automatic rewriting system in the future.

An integrated system for source language checking, analysis and term management
Eric Nyberg | Teruko Mitamura | David Svoboda | Jeongwoo Ko | Kathryn Baker | Jeffrey Micher
Proceedings of Machine Translation Summit IX: System Presentations

This paper presents an overview of the tools provided by KANTOO MT system for controlled source language checking, source text analysis, and terminology management. The steps in each process are described, and screen images are provided to illustrate the system architecture and example tool interfaces.


Deriving semantic knowledge from descriptive texts using an MT system
Eric Nyberg | Teruko Mitamura | Kathryn Baker | David Svoboda | Brian Peterson | Jennifer Williams
Proceedings of the 5th Conference of the Association for Machine Translation in the Americas: Technical Papers

This paper describes the results of a feasibility study which focused on deriving semantic networks from descriptive texts using controlled language. The KANT system [3,6] was used to analyze input paragraphs, producing sentence-level interlingua representations. The interlinguas were merged to construct a paragraph-level representation, which was used to create a semantic network in Conceptual Graph (CG) [1] format. The interlinguas are also translated (using the KANTOO generator) into OWL statements for entry into the Ontology Works electrical power factbase [9]. The system was extended to allow simple querying in natural language.

The KANTOO MT sytem: controlled language checker and lexical maintenance tool
Teriuko Mitamura | Eric Nyberg | Kathy Baker | Peter Cramer | Jeongwoo Ko | David Svoboda | Michael Duggan
Proceedings of the 5th Conference of the Association for Machine Translation in the Americas: System Descriptions

We will present the KANTOO machine translation environment, a set of software servers and tools for multilingual document production. KANTOO includes modules for source language analysis, target language generation, source terminology management, target terminology management, and knowledge source development (see Figure 1).


Pronominal anaphora resolution in KANTOO English-to-Spanish machine translation system
Teruko Mitamura | Eric Nyberg | Enrique Torrejon | David Svoboda | Kathryn Baker
Proceedings of Machine Translation Summit VIII

We describe the automatic resolution of pronominal anaphora using KANT Controlled English (KCE) and the KANTOO English-to-Spanish MT system. Our algorithm is based on a robust, syntax-based approach that applies a set of restrictions and preferences to select the correct antecedent. We report a success rate of 89.6% on a training corpus with 289 anaphors, and 87.5% on held-out data containing 145 anaphors. Resolution of anaphors is important in translation, due to gender mismatches among languages; our approach translates anaphors to Spanish with 97.2% accuracy.