Recent advances in Apertium, a free/open-source rule-based machine translation platform for low-resource languages

Abstract

This paper presents an overview of Apertium, a free and open-source rule-based machine translation platform. Translation in Apertium happens through a pipeline of modular tools, and the platform continues to be improved as more language pairs are added. Several advances have been implemented since the last publication, including some new optional modules: a module that allows rules to process recursive structures at the structural transfer stage, a module that deals with contiguous and discontiguous multi-word expressions, and a module that resolves anaphora to aid translation. Also highlighted is the hybridisation of Apertium through statistical modules that augment the pipeline, and statistical methods that augment existing modules. This includes morphological disambiguation, weighted structural transfer, and lexical selection modules that learn from limited data. The paper also discusses how a platform like Apertium can be a critical part of access to language technology for so-called low-resource languages, which might be ignored or deemed unapproachable by popular corpus-based translation technologies. Finally, the paper presents some of the released and unreleased language pairs, concluding with a brief look at some supplementary Apertium tools that prove valuable to users as well as language developers. All Apertium-related code, including language data, is free/open-source and available at https://github.com/apertium.

GiellaLT: an infrastructure for rule-based language technology tool development

“…The GiellaLT infrastructure supports developing machine translation systems in cooperation with Apertium Khanna et al (2021). The monolingual models developed in the GiellaLT infra are then combined with the transfer rules and lexicons in Apertium to provide an end-to-end MT system.…”

Low-Resource Neural Machine Translation Improvement Using Data Augmentation Strategies

“…Machine translation (MT) is the task of automatically translating text from one language to another. There are three common approaches to MT: rule-based approach [1], statistical-based approach [2,3], and neural-based one [4,5,6]. The rule-based approach depends on translation rules and dictionaries created by human experts.…”