Portable Knowledge Sources for Machine Translation

Portable Knowledge Sources for Machine Translation Koi<'hi ']~ake.d a Tokyo Research Lal>oratory, IBM Research 1623-14 Shimotsurum~t, Yam;~to, K~ulaga,wa. 242, Ja, pa,n Phone: 81-462-73-4569, 81-462-73-7413 (FAX) takedag~trl.vm~t.lbm.com ABSTRACT in this paper, we describe the acquisition iuld (Irga- nization of knowledge sources fur machine translation (MT) systems. It has heen liointed out by many users that one of the most annoying things idmtlt MT sys-- terns is tim repeated occurrence of identical errors in word sense and attachment dlsambiguation. We show the limitations of a conventional user-dictionary method and explain how our approach solves the prol/lem. 1. Introduction In the last decal% more and more commercia.l ma- chine translation (MT) systems have lmcome available for a wide variety (if languag, e Iiairs. An MT system is a very handy tool: trot one quickly Iinds out thai, it Irlakes tt, e same errors over and over again even if a user dictio- nary is carefully maintained. There are sew, ral re;mons for such repeated errors. 1. Commercial MT systems are not tmilt in actor dance with a powerful h;xical semantic formalism. The user dictionary alone cannot (llsamlfiguate word senses and phrasal icttitelimei/ts satisNmtorily. 2. MT systems cannot handle the domain and context dei)endency of word sm,se, ph rasal atl, aeh men L an d word selection. 3. In a shared environment, each user has a differ-. ent user dictionary, and must therefore redumhmtly correct the same errors ms all the other users. A powerful lexieal semantic apl)roaeh [s] couhl give more accurate translatiml~ but it might be. too Inuch to ask users to develop their dictionaries within that formal- ism. Tl, e simple structure of a user dieti(mary also re- stricts the learning ability of M'r systems during the post-editing process. The second of the almw~ re;kstms tl~ motivated recent exanlple-ba~ed and case-b~med ma- chine translation re.search [9, s, 10]. However, a method for finding the best-matehlng eases hi a cime. base., where cases (or exalnples) are collected from different dmna.ins or contexts~ has not been studied well. Nor is it kllown whether considering the frequency of eases gives a bet- ter result. The third reason is rarely <liscussed> hut it is riot desirable sirnply to share a single user dictionary, since the dictionary may become inconsistent by reflect- ing multil>le users' updates. McRoy [s] discussed word sense disambiguation using multlph; knowledge sources, but her method is still dictionary-b~med. Some of the eoolmerclal systems for human-aided trailslatlm h such as the Translation Manager/2 [~1~ can provide the user with nmre Ilexible access to multilile dictionaries and the Iranslation memory (a repository of pairs of smlrce and target sentences). This organiza- tion of knowledge cmdd lie quite useful for selecting cor- rect transhttlons of vvords~ lint the types of knowledge awdlable from the dictionaries and translation memo. ries are rather limited> and are certainly not enough for resolving strueturaJ ambiguities in sentences. In this paper, we propose Porhtble Knowledge Sources (PKSs) for machine translatlou. A PKS consists of preference infi~rmatitm on word sense, l)hrasal …

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