# Transferring Sentiment Cross-Lingually within and across Same-Family Languages
## 4 Related Work
### Cross-Lingual Sentiment Analysis  
In a cross-lingual multi-task learning setup, [PERSON] and [PERSON] [93] performed morphological tagging and language identification by jointly training a BiLSTM with character embeddings. The tagger shared the same tagsets for all languages. [PERSON] et al. [94] studied optimal transfer language selection but did not include sentiment transfer in their setup. In the earliest work in cross-lingual sentiment analysis, [PERSON] et al. [95] utilized resources such as bilingual dictionaries, subjectivity lexicons, and manually translated parallel corpora. Rather than relying on manually translated parallel corpora, [PERSON] et al. [9] investigated this further with automatic translation and cross-lingual projections of subjectivity annotations. It was observed that translating the target dataset into the source language was the preferred approach to training a classifier with translations of source language data into the target language. [PERSON] and [PERSON] [96] employed adversarial training and multilingual language modeling. The English and French language representation models were shared, and language-specific decoders, sentiment classifiers, and language discriminators were trained jointly (DVD and books). Earlier cross-lingual sentiment analysis research focused primarily on translation. In such scenarios, the objective was to translate the target language instances into the source language and perform inference using the source language classifier. The translated instances were also used to train a language tagger with limited resources. [PERSON] et al. [97] introduced the machine translation methodology. [PERSON] et al. [98] demonstrated the efficacy of using machine translation systems when there is insufficient data for the target language. These translations necessitate the existence of a reliable translation system. It has been demonstrated that such systems introduce semantic modifications and errors [23; 99; 100]. Subjectivity indicators used by humans can be lost in translation. [PERSON] [8] merged two distinct perspectives by using Chinese and English translations for a co-training setup. For the task of bilingual lexicon extraction, [PERSON] and [PERSON] [101] used language models trained on comparable corpora to identify and extract words with similar meanings. This was based on the theory that two words are identical if their top semantic word responses are identical. In lexicon-based approaches where supervised resources are scarce, such words are crucial resources. According to [PERSON] et al. [102], multilingual pre-trained models utilizing shared transformers are superior to shared softmax, shared BPE, and anchor points for cross-lingual representations. Cross-domain sentiment analysis research focuses on the acquisition of shared representations across domains and is closely related to cross-lingual sentiment analysis. [PERSON] et al. [103] performed domain-independent feature extraction using domain classification and sentiment classification. Conditional domain adversarial networks [104] incorporated multilinear conditioning of features to enhance the discriminator's performance. Using multi-view representations and a six-layered transformer model with shared encoder and decoder and adversarial training, [PERSON] and [PERSON] [105] aligned data from two distinct languages. The configuration also captures the cross-lingual and cross-domain aspects. The authors used Wikitext to train the model. Compared to the romance languages, the model's performance for Japanese was the worst. Previous research [93; 106; 107] has demonstrated that selecting a hub language from the same language family or one that is closer to the target language in the language family tree facilitates knowledge transfer. [PERSON] et al. [106] utilized fewer instances from Latin languages (French, Spanish, and Portuguese) to improve the performance of machine translation using large parallel English corpora. This also improved performance in the Germanic Dutch language.