  In the online world, Machine Translation (MT) systems are extensively used to
translate User-Generated Text (UGT) such as reviews, tweets, and social media
posts, where the main message is often the author's positive or negative
attitude towards the topic of the text. However, MT systems still lack accuracy
in some low-resource languages and sometimes make critical translation errors
that completely flip the sentiment polarity of the target word or phrase and
hence delivers a wrong affect message. This is particularly noticeable in texts
that do not follow common lexico-grammatical standards such as the dialectical
Arabic (DA) used on online platforms. In this research, we aim to improve the
translation of sentiment in UGT written in the dialectical versions of the
Arabic language to English. Given the scarcity of gold-standard parallel data
for DA-EN in the UGT domain, we introduce a semi-supervised approach that
exploits both monolingual and parallel data for training an NMT system
initialised by a cross-lingual language model trained with supervised and
unsupervised modeling objectives. We assess the accuracy of sentiment
translation by our proposed system through a numerical 'sentiment-closeness'
measure as well as human evaluation. We will show that our semi-supervised MT
system can significantly help with correcting sentiment errors detected in the
online translation of dialectical Arabic UGT.

Computation and Language