EBMT (Example-Based Machine Translation) is proposed. EBMT retrieves similar examples (pairs of source phrases, sentences, or texts and their translations) from a tahase of examples, adapting the examples to franslate a new input. EBMT has the following features: (1) It is easily upgraded simply by inputting appropriate examples to the database; (2) It assigns a reliability factor to the translation result; (3) It is accelerated effectively by both indexing axi parallel computing; (4) It is robust because of best-match reasoning; (5) It well utilizes translator expertise. A prototype system has been implemented to deal with a difficult Iranslation problem fee conventional Rule-Based Machine Translation (RBMT), i.e., translating Japanese noun phrases of the form 'lq a no N2" into English. The system has achieved about a 78% success rate on average. This paper explains the basic idea of EBMT, illustrates the experiment in detail, explains the broad applicability of EBMT to several difficult translation problems fee RBMT discusses the advantages of integrating EBMT with RBMT.

Example-based Machine Translation (EBMT) is a recent approach to MT that offers robustness, scalability and graceful degradation, deriving as it does its competence not from explicit linguistic models of source and target languages, but from the wealth of bilingual corpora that are now available. Gaijin is such a system, employing statistical methods, string-matching, case-based reasoning and template-matching to provide a linguistics-lite EBMT solution. The only linguistics employed by Gaijin is a psycholinguistic constraintthe marker hypothesisthat is minimal, simple to apply, and arguably universal. The scope and current state of Gaijin is described, and some initial evaluation results are reported.

This paper presents a new method of analyzing Japanese noun phrases of the form N1 no N2. The Japanese postposition no roughly corresponds to of, but it has much broader age. The method exploits a definition of N2 in a dictionary. For example, rugby no coach can be interpreted as a person who teaches technique in rugby. We illustrate the effectiveness of the method by the analysis of 300 test noun phrases.

TTL (Translation Template Learner) algorithm learns lexical level correspondences between two translation examples by using analogical reasoning. The sentences used as translation examples have similar and different parts in the source language which must correspond to the similar and different parts in the target language. Therefore these correspondences are learned as translation templates. The learned translation templates are used in the translation of other sentences. However, we need to assign confidence factors to these translation templates to order translation results with respect to previously assigned confidence factors. This paper proposes a method for assigning confidence factors to translation templates learned by the TTL algorithm. Training data is used for collecting statistical information that will be used in confidence factor assignment process. In this process, each template is assigned a confidence factor according to the statistical information obtained from training data. Furthermore, some template combinations are also assigned confidence factors in order to eliminate certain combinations resulting bad translation.

A general definition of rationalist and empiricist natural language processing is attempted. A classification of empiricist machine translation systems is given based on the rationalist/empiricist distinction. Examples of approaches falling into the two different strategies are discussed. Research results are reported from attempts to break new ground in what is referred to as "pure " or non-hybrid example-based machine translation.

Case-based machine translation is a promising approach to resolving problems in rule-based machine translation systems, such as difficulties in control of rules and low adaptability to specific domains. We propose a new mechanism for case-based machine translation, in which a large set of cases is generalized into a large set of cases by using a thesaurus.

The example-based approach to MT is becoming increasingly popular. However, such is the variety of techniques and methods used that it is difficult to discern the overall conception of what examplebased machine translation (EBMT) is and/or what its practitioners conceive it to be. Although definitions of MT systems are notoriously complex, an attempt is made to define EBMT in contrast to other MT architectures (RBMT and SMT).

In this paper, we describe a system and methods for finding structural correspondences from the paired dependency structures of a source sentence and its translation in a target language. The system we have developed finds word correspondences first, then finds phrasal correspon(tences based on word correspondences. We have also developed a GUI system with which a user can check and correct tile correspondences retrieved by the system. These structural correspondences will be used as raw translation I)atterns in a corpus-based translation system. 1

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This paper looks at EBMT from the perspective of the Case-based Reasoning (CBR) paradigm. We attempt to describe the task of machine translation (MT) seen as a potential application of CBR, and attempt to describe MT in standard CBR terms. The aim is to see if other applications of CBR can suggest better ways to approach EBMT.