Solving Thematic Divergences in Machine Translation

SOLVING THEMATIC DIVERGENCES IN MACHINE TRANSLATION Bonnie Doff* M.I.T. Artificial Intelligence Laboratory 545 Technology Square, Room 810 Cambridge, MA 02139, USA internet: bonnie@reagan.ai.mit.edu ABSTRACT Though most translation systems have some mechanism for translating certain types of divergent predicate-argument structures, they do not provide a genera] procedure that takes advantage of the relationship between lexical-semantic struc- ture and syntactic structure. A divergent predicate-argument structure is one in which the predicate (e.g., the main verb) or its arguments (e.g., the subject and object) do not have the same syntactic ordering properties for both the source and target language. To account for such ordering differ- ences, a machine translator must consider language-specific syntactic idiosyncrasies that distinguish a target language ¢rom a source language, while making use of lexical-semantic uniformities that tie the two languages together. This pa- per describes the mechanisms used by the UNITRAN ma- chine translation system for mapping an underlying lexical- conceptual structure to a syntactic structure (and vice ¢erea), and it shows how these mechanisms coupled with a set of gen- eral linking routines solve the problem of thematic divergence in machine translation. 1 INTRODUCTION There are a number of different divergence types that arise during the translation of a source language to a tar- get language. Figure 1 shows some of these divergences with respect to Spanish, English, and German. 1 We will look at each of these traditionally diflicnlt di- vergence types in turn. The first divergence type is a structural divergence in that the verbal object is real- ized as a noun phrase (John) in English and as a prepo- sitional phrase (a Juan) in Spanish. The second diver, *This paper describes research done at the Artificial In- telligence Laboratory of the Massachusetts Institute of Tech- nology. Support for this research has been provided by NSF Grant DCR-85552543 under a Presidential Young Investiga- tor's Award to Professor Robert C. Berwick. Useful guidance and commentary during this research were provided by Bob Berwick, Noam Chomsky, Bruce Dawson, Ken Hale, Mike Kashket, Jeff Siskind, and Patrick Winston. The author is also indebted to three anonymous reviewers for their aid in reshaping this paper into its current form. 1Many sentences may fit into these divergence classes, not just the ones listed here. Also, a single sentence may exhibit any or all of these divergences. Divergence Translation Type Ezample Structural Conflational Lexical Categorial Thematic I saw John Via Juan (I saw to John) I like Mary Ich habe Marie gem (I have Mary likingly) I stabbed John Yo le di pufialadas a Juan (I gave knife-wounds to John) I am hungry Ieh habe Hunger (I have hunger) I like Mary Maria me gusta a mf (Mary pleases me) Figure 1: Divergence Types in Machine Translation gence is conttational. Conflation is the incorporation of necessary participants (or arguments) of a given action. Here, English uses the single word like for the two Ger- man words haben (have) and gem (likingly); this is be- cause the manner argument (i.e., the likingly portion …

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