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

arXiv: https://arxiv.org/abs/2210.11899 · HTML: https://mlsanity.com/paper/2210.11899/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Unsupervised Machine Translation Using Monolingual Corpora Only](https://mlsanity.com/paper/1711.00043/) (2017)
- [Unsupervised Neural Machine Translation](https://mlsanity.com/paper/1710.11041/) (2017)
- [Phrase-Based & Neural Unsupervised Machine Translation](https://mlsanity.com/paper/1804.07755/) (2018)
- [An Effective Approach to Unsupervised Machine Translation](https://mlsanity.com/paper/1902.01313/) (2019)
- [LABR: A Large Scale Arabic Sentiment Analysis Benchmark](https://mlsanity.com/paper/1411.6718/) (2014)
- [BLEU, METEOR, BERTScore: Evaluation of Metrics Performance in Assessing Critical Translation Errors in Sentiment-oriented Text](https://mlsanity.com/paper/2109.14250/) (2021)
- [Is it Great or Terrible? Preserving Sentiment in Neural Machine Translation of Arabic Reviews](https://mlsanity.com/paper/2010.13814/) (2020)
- [SentiWords: Deriving a High Precision and High Coverage Lexicon for Sentiment Analysis](https://mlsanity.com/paper/1510.09079/) (2015)
- [Challenges in Translation of Emotions in Multilingual User-Generated Content: Twitter as a Case Study](https://mlsanity.com/paper/2106.10719/) (2021)
- [Sentiment-Aware Measure (SAM) for Evaluating Sentiment Transfer by Machine Translation Systems](https://mlsanity.com/paper/2109.14895/) (2021)

## Built on by (most cited first, 2 of 2)

- [Natural Language Processing for Dialects of a Language: A Survey](https://mlsanity.com/paper/2401.05632/) (2024)
- [Dhati+: Fine-tuned Large Language Models for Arabic Subjectivity Evaluation](https://mlsanity.com/paper/2508.19966/) (2025)

## Abstract (arXiv)

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.