RuLearn: an Open-source Toolkit for the Automatic Inference of Shallow-transfer Rules for Machine Translation

Abstract

This paper presents ruLearn, an open-source toolkit for the automatic inference of rules for shallow-transfer machine translation from scarce parallel corpora and morphological dictionaries. ruLearn will make rule-based machine translation a very appealing alternative for under-resourced language pairs because it avoids the need for human experts to handcraft transfer rules and requires, in contrast to statistical machine translation, a small amount of parallel corpora (a few hundred parallel sentences proved to be sufficient). The inference algorithm implemented by ruLearn has been recently published by the same authors in Computer Speech & Language (volume 32). It is able to produce rules whose translation quality is similar to that obtained by using hand-crafted rules. ruLearn generates rules that are ready for their use in the Apertium platform, although they can be easily adapted to other platforms. When the rules produced by ruLearn are used together with a hybridisation strategy for integrating linguistic resources from shallow-transfer rule-based machine translation into phrase-based statistical machine translation (published by the same authors in Journal of Artificial Intelligence Research, volume 55), they help to mitigate data sparseness. This paper also shows how to use ruLearn and describes its implementation.

PE2LGP: tradutor de português europeu para língua gestual portuguesa em glosas

“…Antes de passarà construção da gramática, há que alinhar o léxico das frases do corpus. Em sistemas de tradução estatísticos, o alinhamento do léxicoé usualmente calculado através de métodos probabilísticos (Tambouratzis et al, 2012;Chiu et al, 2007;Sánchez-Cartagena et al, 2016), contudo no caso do par de línguas português-LGP, não existe um corpus suficientemente grande para treinar o alinhamento entre palavras e gestos. Assim, propomos um método baseado em medidas de semelhança (string matching e semelhança semântica), que se descreve de seguida.…”