This paper describes an accurate and robust text alignment system for structurally different languages. Among structurally different languages such as Japanese and English, there is a limitation on the amount of word correspondences that can be statistically acquired. The main reason for this is the systems of functional (closed) words are quite different in the two languages. The proposed method makes use of two kinds of word correspondences in aligning bilingual texts. One is a bilingual dictionary of general use. The other is the word correspondences that are statistically acquired in the alignment process. Our method gradually determines sentence pairs (anchors) that correspond to each other by relaxing parameters. The method, by combining two kinds of word correspondences, achieves adequate word correspondences for complete alignment. As a result, texts of various length and of various genres in structurally different languages can be aligned with high precision. Experimental results show our system outperforms conventional methods for various kinds of Japanese–English texts.

Natural language generation is now moving away from research prototypes into more practical applications. Generation functionality is also being asked to play a more significant role in established applications such as machine translation. In both cases, multilingual generation techniques have much to offer. However, the take-up of multilingual generation is being restricted by a critical lack both of large-scale linguistic resources suited to the generation task and of appropriate development environments. This paper describes KPML, a multilingual development environment that offers one possible solution to these problems. KPML aims to provide generation projects with standardized, broad-coverage, reusable resources and a basic engine for using such resources for generation. A variety of focused debugging aids ensure efficient maintenance, while supporting multilingual work such as contrastive language development and automatic merging of independently developed resources. KPML is based on a new, generic approach to multilinguality in resource description that extends significantly beyond previous approaches. The system has already been used in a number of large generation projects and is freely available to the generation community.

In this article, we give an overview of Natural Language Generation (NLG) from an applied system-building perspective. The article includes a discussion of when NLG techniques should be used; suggestions for carrying out requirements analyses; and a description of the basic NLG tasks of content determination, discourse planning, sentence aggregation, lexicalization, referring expression generation, and linguistic realisation. Throughout, the emphasis is on established techniques that can be used to build simple but practical working systems now. We also provide pointers to techniques in the literature that are appropriate for more complicated scenarios.