Issue No. 04 - April (1981 vol. 3)
Jaime G. Carbonell , Artificial Intelligence Project, Department of Computer Science, Yale University, New Haven, CT 06520; Department of Computer Science, Carnegie-Mellon University, Pittsburgh, PA 15
Richard E. Cullingford , MEMBER, IEEE, Artificial Intelligence Project, Department of Computer Science, Yale University, New Haven, CT 06520; Department of Electrical and Computer Science, University of Co
Anatole V. Gershman , Artifical Intelligence Project, Department of Computer Science, Yale University, New Haven, CT 06520; Schlumberger Research Laboratories, Ridgefield, CT 06877.
This paper considers the possibilities for knowledge-based automatic text translation in the light of recent advances in artificial intelligence. It is argued that competent translation requires some reasonable depth of understanding of the source text, and, in particular, access to detailed contextual information. The following machine translation paradigm is proposed. First, the source text is analyzed and mapped into a language-free conceptual representation. Inference mechanisms then apply contextual world knowledge to augment the representation in various ways, adding information about items that were only implicit in the input text. Finally, a natural-language generator maps appropriate sections of the language-free representation into the target language. We discuss several difficult translation problems from this viewpoint with examples of English-to-Spanish and English-to-Russian translations; and illustrate possible solutions as embodied in a computer understander called SAM, which reads certain kinds of newspaper stories, then summarizes or paraphrases them in a variety of languages.
J. G. Carbonell, R. E. Cullingford and A. V. Gershman, "Steps Toward Knowledge-Based Machine Translation," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 3, no. , pp. 376-392, 1981.