Inducing translation templates with type constraints

This paper presents a generalization technique that induces translation templates from a given set of translation examples by replacing differing parts in the examples with typed variables. Since the type of each variable is inferred during the learning process, each induced template is also associated with a set of type constraints. The type constraints that are associated with a translation template restrict the usage of the translation template in certain contexts in order to avoid some of the wrong translations. The types of variables are induced using type lattices designed for both the source and target languages. The proposed generalization technique has been implemented as a part of an example-based machine translation system. © Springer Science+Business Media 2007

Improving the precision of example-based machine translation by learning from user feedback

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2007.Thesis (Master's) -- Bilkent University, 2007.Includes bibliographical references leaves 110-113Example-Based Machine Translation (EBMT) is a corpus based approach to Machine Translation (MT), that utilizes the translation by analogy concept. In our EBMT system, translation templates are extracted automatically from bilingual aligned corpora, by substituting the similarities and differences in pairs of translation examples with variables. As this process is done on the lexical-level forms of the translation examples, and words in natural language texts are often morphologically ambiguous, a need for morphological disambiguation arises. Therefore, we present here a rule-based morphological disambiguator for Turkish. In earlier versions of the discussed system, the translation results were solely ranked using confidence factors of the translation templates. In this study, however, we introduce an improved ranking mechanism that dynamically learns from user feedback. When a user, such as a professional human translator, submits his evaluation of the generated translation results, the system learns “contextdependent co-occurrence rules” from this feedback. The newly learned rules are later consulted, while ranking the results of the following translations. Through successive translation-evaluation cycles, we expect that the output of the ranking mechanism complies better with user expectations, listing the more preferred results in higher ranks. The evaluation of our ranking method, using the precision value at top 1, 3 and 5 results and the BLEU metric, is also presented.Daybelge, Turhan OsmanM.S

Inducing Translation Templates with Type Constraints

Abstract. This paper presents a generalization technique that induces translation templates from a given set of translation examples by replacing differing parts in the examples with typed variables. Since the type of each variable is inferred during the learning process, each induced template is also associated with a set of type constraints. The type constraints that are associated with a translation template restrict the usage of the translation template in certain contexts in order to avoid some of the wrong translations. The types of variables are induced using type lattices designed for both the source language and the target language. The proposed generalization technique has been implemented as a part of an EBMT system