Incremental Translation Utilizing Constituent Boundary Patterns

Incremental Translation Utilizing Constituent Boundary Patterns Osamu FURUSE, Hitoshi IIDA ATR Interpreting Telecommunications Research Laboratories 2-2 tlikaridai, Seika-cho, Soraku-gun, Kyoto, 619-02, Japan {furuse, iida}@itl.atr.co.jp Abstract We have proposed an incremental trans- lation method in Transfer-Driven Ma- chine Translation (TDMT). In this method, constituent boundary patterns are applied to an input in a bottom-up fashion. Also, by dealing with best-only substructures, the explosion of structural ambiguity is constrained and an efficient translation of a lengthy input can be achieved. Through preliminary exper- imentation our new TDMT has been shown to be more efficient while main- tMning translation quality. 1 Introduction A system dealing with spoken language requires a quick response in order to provide smooth com- munication between humans or between a hu- man and a computer. Thereibre, assuring effi- ciency in spoken-language translation is one of the most crucial tasks in devising such a system. In spoken language, the translation of lengthy utterances can yield a huge amount of struc- tural ambiguity, which needs to be efficiently pro- cessed by the system. As a solution for achiev- ing an efficient spoken-language system, several techniques, such as incremental generation (Fin- kler, 1992; Kempen, 1987) and marker-passing memory-based translation (Kitano, 1994), have been proposed. Many of these techniques adopt a left-to-right strategy to handle an input incre- mentally and a best-first strategy to avoid the ex- plosion of structural ambiguity. These strategies (:an be achieved with bottom-up processing. We have already proposed Transfer-Driven Ma- chine Translation (TDMT) for efficient and ro- bust spoken-language translation (Furuse, 1994a; Furuse, 1994b). However, the top-down and breadth-first translation strategy in the earlier versions of TDMT, which yields a quick response for inputs with restricted lengths, may show poor efficiency when processing a very lengthy input or inputs having many competing structures. In a top-down and breadth-first application, all the possible structures are retained until the whole input string is parsed. This requires many compu- tations and results in inefficient translation. For instance, the sentence below has many competing structures, mainly because of possible combina- tions within noun sequences. If this expression is combined with another expression, the structurM ambiguity will be further compounded. With bacon chicken eggs lettuce and tomato on it. In contrast, if structural ambiguities of sub- strings are always settled and are never inherited to the upper structures, the explosion of struc- turM ambiguity could be constrained. Thus, an incremental strategy that fixes partial results is necessary for efficient processing and is achieved by bottom-up processing in left-to-right order. This paper proposes TDMT using an incremen- tal strategy for achieving efficient translation of a lengthy input or one having a lot of structural ambiguity. In this method, several constituent boundary patterns are applied to an input string in a bottom-up fashion. This bottom-up applica- tion, based on the concept of chart parsing, can constrain the explosion of structural ambiguity by dealing with best-only substructures using seman- tic distance calculations. In this paper, we will first; outline our new trans- lation strategy. We will …

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