Machine Translation has more and more become an essential method to assist or even replace human translators. The necessity of developing useful computer software that fulfils this task has grown because in the age of the internet people want to get their information in their own language. Which approach is appropriate and which technique works well in order to cope with this challenge?
This paper will focus on Example-based Machine Translation (EBMT), an approach that does not correspond with traditional translation systems but has the advantage of requiring only little knowledge and thus being usable in a great number of languages.
Table of Contents
- 0 Introduction
- 1 Procedure
- 1.1 History
- 1.2 EBMT: Definition
- 1.3 Comparison to Other Approaches
- 2 Critical Evaluation
- 2.1 General Problems
- 2.2 Advantages
- 2.3 Practical Use
Objectives and Key Themes
This paper aims to explore Example-based Machine Translation (EBMT), an alternative approach to traditional machine translation systems. It examines the history, definition, and procedures of EBMT, comparing it to other methodologies like statistical and rule-based machine translation. The paper also critically evaluates the advantages and disadvantages of EBMT and its practical applicability.
- Definition and History of EBMT
- Comparison of EBMT with other Machine Translation approaches
- Procedure and Mechanisms of EBMT
- Critical Evaluation of EBMT's strengths and weaknesses
- Practical Use and Applicability of EBMT
Chapter Summaries
0 Introduction: This introductory chapter sets the stage by highlighting the increasing need for efficient machine translation in the digital age. It introduces Example-based Machine Translation (EBMT) as a less resource-intensive alternative to traditional methods, promising applicability across numerous languages. The chapter outlines the paper's structure, promising to define EBMT, trace its history, compare it to other approaches, detail its procedure, provide illustrative examples, and finally, critically evaluate its efficacy and practical use. The overall tone establishes EBMT as a promising area of research warranting detailed investigation.
1 Procedure: This chapter delves into the mechanics of EBMT. It begins with a historical overview, tracing the evolution of machine translation from early experiments in the 1950s to the emergence of corpus-based methods like EBMT in the 1980s as a response to the limitations of rule-based systems. The core concept of EBMT—using a bilingual database of example translations to construct new translations by combining existing phrases—is clearly defined and illustrated with examples. The chapter contrasts EBMT's approach to that of statistical machine translation and translation memory, highlighting the automated nature of example selection in EBMT as its key distinguishing feature. This section lays the groundwork for understanding how EBMT functions technically.
2 Critical Evaluation: This chapter offers a thorough assessment of EBMT, addressing both its strengths and weaknesses. The discussion of general problems likely includes challenges related to finding sufficiently similar examples in the database, handling ambiguities, and ensuring accurate translation of nuanced expressions. The advantages of EBMT are likely highlighted, such as its relative ease of implementation and adaptability to various language pairs due to its reliance on example data rather than intricate linguistic rules. The final part of the chapter would assess the practical applicability of EBMT, considering factors such as the size and quality of the required bilingual database, the computational resources needed, and the overall performance in real-world translation scenarios. This critical analysis completes the paper's examination of EBMT.
Keywords
Example-based Machine Translation (EBMT), Corpus-based MT, Statistical Machine Translation (SMT), Rule-based Machine Translation (RBMT), Translation Memory (TM), Bilingual Database, Phrase Extraction, Phrase Combination, Computational Linguistics.
Example-Based Machine Translation (EBMT): A Comprehensive Overview - FAQ
What is this document about?
This document provides a comprehensive overview of Example-Based Machine Translation (EBMT). It covers the history, definition, procedures, and critical evaluation of EBMT, comparing it to other machine translation approaches such as statistical and rule-based methods. The document includes a table of contents, chapter summaries, objectives and key themes, and keywords.
What is Example-Based Machine Translation (EBMT)?
EBMT is a machine translation approach that uses a bilingual database of example translations. New translations are constructed by combining existing phrases from this database. It's considered a less resource-intensive alternative to traditional methods like rule-based and statistical machine translation.
What are the key themes explored in this document?
The key themes include the definition and history of EBMT, a comparison of EBMT with other machine translation approaches (Statistical Machine Translation (SMT) and Rule-based Machine Translation (RBMT)), the procedures and mechanisms of EBMT, a critical evaluation of EBMT's strengths and weaknesses, and its practical use and applicability.
How does EBMT compare to other Machine Translation approaches (SMT and RBMT)?
The document contrasts EBMT with statistical machine translation and rule-based machine translation, highlighting the automated nature of example selection in EBMT as a key distinguishing feature. Specific differences in approach and resource requirements are discussed in detail.
What are the advantages and disadvantages of EBMT?
The document critically evaluates EBMT, addressing both its advantages (such as relative ease of implementation and adaptability to various language pairs) and disadvantages (challenges related to finding sufficiently similar examples, handling ambiguities, and ensuring accurate translation of nuanced expressions). The practical applicability is assessed considering factors like the size and quality of the required bilingual database and computational resources.
What are the procedures involved in EBMT?
The document details the mechanics of EBMT, including a historical overview tracing its evolution, a clear definition of its core concept (using a bilingual database), and illustrations of how it functions technically. This includes the processes of phrase extraction and combination.
What is the historical context of EBMT?
The document traces the history of EBMT, situating its emergence in the 1980s as a response to the limitations of rule-based systems, within the broader context of machine translation's development from early experiments in the 1950s.
What are the key keywords associated with this document?
Key keywords include Example-based Machine Translation (EBMT), Corpus-based MT, Statistical Machine Translation (SMT), Rule-based Machine Translation (RBMT), Translation Memory (TM), Bilingual Database, Phrase Extraction, Phrase Combination, and Computational Linguistics.
What is the structure of the document?
The document is structured into three main chapters: an introduction, a chapter detailing the procedure of EBMT, and a chapter providing a critical evaluation. Each chapter is summarized within the document.
What is the overall conclusion of the document regarding EBMT?
The document offers a comprehensive and balanced assessment of EBMT, highlighting its potential while acknowledging its limitations. The final evaluation considers its practical applicability and overall efficacy in real-world translation scenarios.
- Quote paper
- Stefanie Dietzel (Author), 2007, Example-based Machine Translation, Munich, GRIN Verlag, https://www.grin.com/document/133406
