Example-based machine translation based on the synchronous SSTC annotation schema

MT Summit VII Sept. 1999 Example-Based Machine Translation Based on the Synchronous SSTC Annotation Schema Mosleh H. Al-Adhaileh and Tang Enya Kong Computer Aided Translation Unit School of Computer Sciences Universiti Sains Malaysia 11800 PENANG, MALAYSIA mosleh@cs.usm.my, enyakong@cs.usm.my Abstract In this paper, we describe an Example-Based Machine Translation (EBMT) system for English- Malay translation. Our approach is an example- based approach which relies sorely on example translations kept in a Bilingual Knowledge Bank (BKB). In our approach, a flexible annotation schema called Structured String-Tree Correspondence (SSTC) is used to annotate both the source and target sentences of a translation pair. Each SSTC describes a sentence, a representation tree as well as the correspondences between substrings in the sentence and subtrees in the representation tree. With both the source and target SSTCs established, a translation example in the BKB can then be represented effectively in terms of a pair of synchronous SSTCs. In the process of translation, we first try to build the representation tree for the source sentence (English) based on the example-based parsing algorithm as presented in [1]. By referring to the resultant source parse tree, we then proceed to synthesis the target sentence (Malay) based on the target SSTCs as pointed to by the synchronous SSTCs which encode the relationship between source and target SSTCs. Keywords: Structured String-Tree Correspondence (SSTC), Example-based Machine Translation (EBMT), Bilingual Knowledge Base (BKB). 1 Introduction Here, we design an approach for Machine Translation (MT) which depends on related translated examples kept in a Bilingual Knowledge Bank (BKB). This approach is called example-based MT; the linguistic knowledge extracted directly from the example-base will be used to analyze and translate a source sentence to the corresponding target sentence. Ideally if the sentence is already in the example-base, the translation is found there too, but in most cases, the source sentence will not be found in the example-base. In such case, a method is used to retrieve close related examples and use the knowledge from these examples to construct the translation for the source sentence. In general, this approach relies on the assumption that if two source sentences are “close”, their translations should be “close” too; if the translation of the first one is known, the translation of the other can be obtained by making some modifications in the translation of the first one [4]. The example-based approach has become a common technique for NLP applications, especially in MT as reported in [6], [9] and [7]. However, a main problem normally arises in the current approaches which indirectly limits their applications in the development of a large scale and practical example-based MT system, i.e. the lack of flexibility in representing translation relations between source and target substrings where the substrings being possibly discontinuous in both cases. In this paper, we propose to overcome the problem by introducing a flexible annotation schema called synchronous Structured String-Tree Correspondence (SSTC) which will be used to annotate translation examples in the Bilingual Knowledge Bank (BKB). We will also present a …

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