No abstract

Black-box machine translation systems have proven incredibly useful for a variety of applications yet by design are hard to adapt, tune to a specific domain, or build on top of. In this work, we introduce a method to improve such systems via automatic pre-processing (APP) using sentence simplification. We first propose a method to automatically generate a large in-domain paraphrase corpus through back-translation with a black-box MT system, which is used to train a paraphrase model that “simplifies” the original sentence to be more conducive for translation. The model is used to preprocess source sentences of multiple low-resource language pairs. We show that this preprocessing leads to better translation performance as compared to non-preprocessed source sentences. We further perform side-by-side human evaluation to verify that translations of the simplified sentences are better than the original ones. Finally, we provide some guidance on recommended language pairs for generating the simplification model corpora by investigating the relationship between ease of translation of a language pair (as measured by BLEU) and quality of the resulting simplification model from back-translations of this language pair (as measured by SARI), and tie this into the downstream task of low-resource translation.

No abstract

Recent years have shown an increase in both in the number and use of online educational learning environments. Correspondingly, there is a greater availability of rich data sets that describe both the learners themselves and their interactions with the online learning environment. In this paper, we demonstrate the use of a data mining tool, association analysis, to analyze this data. We demonstrate its applicability in understanding how learners use a particular online learning environment and for the identification of learner interactions with the environments that are associated with particular learning outcomes. The methodology is first described and then is demonstrated as a case study through its application to the CareerWISE online learning environment.

No abstract