Learning Translation Rules From A Bilingual Corpus

TL;DR

This paper introduces a method for learning translation rules from bilingual corpora by using analogical reasoning to identify correspondences between sentence parts, aiming to improve translation rule extraction.

Contribution

It presents a novel mechanism that learns translation rules based on analogical reasoning between sentence pairs, enhancing the understanding of cross-language correspondences.

Findings

System tested on small dataset shows promising results.

Method effectively captures similarities and differences in sentence pairs.

Provides a foundation for further research in translation rule learning.

Abstract

This paper proposes a mechanism for learning pattern correspondences between two languages from a corpus of translated sentence pairs. The proposed mechanism uses analogical reasoning between two translations. Given a pair of translations, the similar parts of the sentences in the source language must correspond the similar parts of the sentences in the target language. Similarly, the different parts should correspond to the respective parts in the translated sentences. The correspondences between the similarities, and also differences are learned in the form of translation rules. The system is tested on a small training dataset and produced promising results for further investigation.