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.

翻译：在在线世界中，机器翻译系统被广泛用于翻译用户生成文本，如评论、推文和社交媒体帖子，这些文本的主要信息通常是作者对文本主题的正面或负面态度。然而，机器翻译系统在一些低资源语言中仍然缺乏准确性，有时会犯下关键翻译错误，完全翻转目标词或短语的情感极性，从而传递错误的情感信息。这在不符合常见词汇-语法标准的文本（例如在线平台上使用的阿拉伯方言）中尤为明显。本研究旨在改善用阿拉伯方言版本写成的用户生成文本翻译成英文时的情感翻译质量。鉴于用户生成文本领域中黄金标准阿拉伯方言-英文平行数据的稀缺，我们引入了一种半监督方法，该方法利用单语和平行数据训练神经机器翻译系统，该系统由一个通过监督和无监督建模目标训练的跨语言语言模型初始化。我们通过数值“情感接近度”指标以及人工评估来评估我们提出的系统在情感翻译方面的准确性。我们将展示，我们的半监督机器翻译系统能够显著帮助纠正阿拉伯方言用户生成文本在线翻译中检测到的情感错误。