Low-cost, High-performance Translation Retrieval: Dumber is Better

Low-cost, High-performance Translation Retrieval: Dumber is Better Timothy Baldwin Department of Computer Science Tokyo Institute of Technology 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552 JAPAN tim@cl.cs.titech.ac.jp Abstract In this paper, we compare the rela- tive effects of segment order, segmen- tation and segment contiguity on the retrieval performance of a translation memory system. We take a selec- tion of both bag-of-words and segment order-sensitive string comparison meth- ods, and run each over both character- and word-segmented data, in combina- tion with a range of local segment con- tiguity models (in the form of N-grams). Over two distinct datasets, we find that indexing according to simple character bigrams produces a retrieval accuracy superior to any of the tested word N- gram models. Further, in their optimum configuration, bag-of-words methods are shown to be equivalent to segment order- sensitive methods in terms of retrieval accuracy, but much faster. We also pro- vide evidence that our findings are scal- able. 1 Introduction Translation memories (TMs) are a list of translation records (source language strings paired with a unique target language translation), which the TM system accesses in suggesting a list of target language (L2) translation candi- dates for a given source language (L1) input (Tru- jillo, 1999; Planas, 1998). Translation retrieval (TR) is a description of this process of selecting from the TM a set of translation records (TRecs) of maximum L1 similarity to a given input. Typi- cally in example-based machine translation, either a single TRec is retrieved from the TM based on a match with the overall L1 input, or the input is partitioned into coherent segments, and indi- vidual translations retrieved for each (Sato and Nagao, 1990; Nirenburg et al., 1993); this is the first step toward generating a customised transla- tion for the input. With stand-alone TM systems, on the other hand, the system selects an arbitrary number of translation candidates falling within a certain empirical corridor of similarity with the overall input string, and simply outputs these for manual manipulation by the user in fashioning the final translation. A key assumption surrounding the bulk of past TR research has been that the greater the match stringency/linguistic awareness of the retrieval mechanism, the greater the final retrieval accu- racy will become. Naturally, any appreciation in retrieval complexity comes at a price in terms of computational overhead. We thus follow the lead of Baldwin and Tanaka (2000) in asking the ques- tion: what is the empirical effect on retrieval per- formance of different match approaches? Here, retrieval performance is defined as the combina- tion of retrieval speed and accuracy, with the ideal method offering fast response times at high accu- racy. In this paper, we choose to focus on retrieval performance within a Japanese–English TR con- text. One key area of interest with Japanese is the effect that segmentation has on retrieval performance. As Japanese is a non-segmenting language (does not explicitly delimit words or- thographically), we can take the brute-force ap- proach in treating each string as a sequence of characters …

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