Interaction between Structural Changes in Machine Translation

1. I n t r o d u c t i o n Several focal issues have emerged in machine translation (MT) research as the result of recent intensive studies in the field. Tha t is, • Declarative Frameworks for expressing Bi-lingual Knowledge • Integration of Knowledge-based Processing and Contextual Processing with the Translation Process • Effective Exploitation of Domain /Tex t Type Specificity (or Sublanguageness) in MT and Discovery Processes for such Specificities Though new frameworks of MT such as Statistics-based MT (SBMT), Example-based MT (EBMT), Analogybased MT (ABMT), Knowledge-based MT (KBMT) etc. look radically different from conventional linguisticbased MT (LBMT) such as Transfer-based, they address one or two of the above focal issues and ignore the rest [7] [9]. In particular, the new paradigms of MT tend to ignore the first issue ie. declarative representation of bi-lingual knowledge and the complexities involved n the process of combining units of bi-lingual knowledge. It is our contention that any MT system, whichever paradigm it belongs to, has to store a set of translationequivalent units for a pair of languages and combine these to produce larger units of translation. In EBMT, for example, a set of translation examples has to be stored and several of them have to be combined properly to produce translation. Because of the declarative nature of translation examples, EBMT inevitably encounters the same complexities of combining translation units in declarative representation as LBMT does. Research in LBMT [1] [2] [31 [10] [12] has revealed that difficulties in the declarative representation of bi-lingual knowledge stem mainly from the t rea tment of idiosyncratic structural changes caused by lexical items, and interactions of such idiosyncratic structural changes when they co-exist in single sentences. These types of structural changes also cause problems when they are combined with general or ordinary linguistic phenomena such as coordination. A formal framework to cope with these mat ters is essential in other approaches, such as EBMT, as in LBMT, if the translation is produced in a compositional way. In this paper, we discuss problems caused by such interaction, and give solutions in our logic-based transfer framework [8]. Our transfer model views the transfer process as deduction; it produces the target linguistic description without changing the source description. This gives a clear contrast to the conventional tree-transducer model, which gradually transforms the source description, and is therefore hard to control. Because of the logic oriented nature of out framework, it can also be extended to cope with the other focal issue of MT such as the integration of knowledge-based processing with translation[4]. 2. F o r m a l i s m We use a simple formalism, representing a sentence as a network of semantic dependencies. The basic units of the formalism are indices, properties, and relations. A logical form consists of an unordered set of terms; each term is either a property predicated of an index, or a relation between two indices. The written notation depicts properties and relations as unary and binary functors, respectively, and indices as their arguments, i.e. within brackets. A logical form representing ' John saw Mary ' might be e : john(j) ~5 see(e) ~ mary (m) & tense(e,past) &