Browsing by Subject "Example-Based Machine Translation"

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Open Access
Error-tolerant retrieval of trees
(IEEE, 1997) Oflazer, K.
We present an efficient algorithm for retrieving from a database of trees, all trees that differ from a given query tree by a small number additional or missing leaves, or leaf label changes. It has natural language processing applications in searching for matches in example-based translation systems, and retrieval from lexical databases containing entries of complex feature structures. For large randomly generated synthetic tree databases (some having tens of thousands of trees), and on databases constructed from Wall Street Journal treebank, it can retrieve for trees with a small error, in a matter of tenths of a second to about a second. © 1997 IEEE.
Open Access
Improving the precision of example-based machine translation by learning from user feedback
(2007) Daybelge, Turhan Osman
Example-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.