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

A Semi-supervised Approach for a Better Translation of Sentiment in Dialectical Arabic UGT