Example-Based Machine Translation in the Pangloss System

Example-Based Machine Translation in the Pangloss System Ralf D. Brown (Jenter for Machine Translation (~arncgie Mellon [lniversity 5000 Fort)('s Aw;nuc l'ittsburgll, PA 15213-',~890 ralf@cs, cmu. edu Abstract The Pangloss Example-Based Machine Translation engine (I'anEI3MT) l is a translation system reql,iring essentially no knowledge of the structure of a lan- guage, merely a large parallel corpus of example sentences atn[ a bilingual dictio- nary. Input texts are segmented into se- quences of words occurring in the corpus, for which translations are determined by subsententia[ alignment of the sentence pairs containing those sequences. These partial translations are then combined with the results of other translation en gines to form the final translation pro- duced by the Pangloss system. In an internal evaluation, PanEBMT achieved 70.2% coverage of unrestricted Spanish news-wire text, despite a simplistic sub- sententia[ alignment algorithm, a subop ritual dictionary, and a corpus Dora a dif- ferent domain than the evalual, ion texts. 1 Introduction Pangloss (Nirenburg el; al., 1995) is a multi- engine machine translation system, in which sev- eral translation engines are. run in parallel to pro- pose translations of various portions of the input, Dora which the final translation is selected by a statistical language model. Panl'3BMT is one of the translation engines used by Pangloss. EBMT is essentially translation-by-analogy: given a source-language passage S and a collec- tion of aligned source/target text pairs, lind the "best" match for S in the source-language half of the text collection, and accept the target-language half of that match as the translation. PanEBMT, like other example-based translation systems, uses essentially no knowledge about its source or target languages; what little knowledge it does use is op- tional, and is supplied in a eonIiguration file. Its 1This work as part of the l'angloss project was sup- ported I)y tim U.S. I)epartment of Defense three main knowledge sources arc: a sententially- aligned parallel bilingual corpus; a bilingual dic- tionary; and a target-language root/synonym list,. The fourth (minor and optional) knowledge source is the hmguage-specific information provided in the conliguration tile, which consists of n list of tokenizations equating words within classes such as w0ekdays, a list of words which ntay be elided during alignment (such as artMes), and a list of words which may be inserted 2 Parallel Bilingual Corpus The corpus used by PanEBMT consists of a set of source/target sentence, pairs, and is flflly indexed on t, he source-language sentences. The corpus is not aligned at any granularity liner than the sen- tence pair; subsententia] alignment is perfornled at run-time based on the sentence fragments se- let;ted and the other knowledge sources. The corpus index lists all occurrences of ev- ery word and punctuation mark in the source- language sentences contained in the corpus. The index has been designed to permit incremental up- dates, allowing new sentence pairs to be added to the corpus as they become awulable (for example, to implement a translation memory with the sys- tem's own output). The text is tokenized prior to indexing, so that …

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