Case-Based Translation: First Steps from a Knowledge-Light Approach Based on Analogy to a Knowledge-Intensive One

Abstract

This paper deals with case-based machine translation. It is based on a previous work using a proportional analogy on strings, i.e., a quaternary relation expressing that “String A is to string B as string C is to string D”. The first contribution of this paper is the rewording of this work in terms of case-based reasoning: a case is a problem-solution pair $(A, A')$ where A is a sentence in an origin language and $A'$, its translation in the destination language. First, three cases $(A, A')$, $(B, B')$, $(C, C')$ such that “A is to B as C is to the target problem D” are retrieved. Then, the analogical equation in the destination language “$A'$ is to $B'$ as $C'$ is to x” is solved and $D'=x$ is a suggested translation of D. Although it does not involve any linguistic knowledge, this approach was effective and gave competitive results at the time it was proposed. The second contribution of this work aims at examining how this prior knowledge-light case-based machine translation approach could be improved by using additional pieces of knowledge associated with cases, domain knowledge, retrieval knowledge, and adaptation knowledge, and other principles or techniques from case-based reasoning and natural language processing.

The first author is supported by a JSPS Grant-In-Aid 18K11447: “Self-explainable and fast-to-train example-based machine translation using neural networks.”