Pattern-Based Context-Free Grammars for Machine Translation

TL;DR

This paper introduces pattern-based context-free grammars to enhance machine translation systems, focusing on customization, efficiency, and scalability to meet diverse user needs.

Contribution

It presents a novel approach using pattern-based grammars for MT, addressing key requirements like customization, efficiency, and incremental improvement.

Findings

Effective customization for various domains

Efficient translation algorithms demonstrated

Scalable system with incremental quality improvements

Abstract

This paper proposes the use of ``pattern-based'' context-free grammars as a basis for building machine translation (MT) systems, which are now being adopted as personal tools by a broad range of users in the cyberspace society. We discuss major requirements for such tools, including easy customization for diverse domains, the efficiency of the translation algorithm, and scalability (incremental improvement in translation quality through user interaction), and describe how our approach meets these requirements.