# Cross-Lingual Cross-Domain Transfer Learning for
## 2 Related Work
### Cross-Lingual Learning  
Cross-lingual learning has been applied to several NLP tasks, including misinformation detection for e-commerce [78], hate speech detection [79], and emotion detection [23]. For example, [PERSON] et al. [21] adopted a multi-task learning strategy to minimize reliance on large annotated datasets, targeting POS tagging, NER, and language modeling. Their model employed shared neural layers between languages and tasks, using character-level convolutional embeddings (CharNN) and pre-trained embeddings for Spanish and English, with extensions to Russian, Dutch, and Chechen. Their alternating training strategy mirrored that of [PERSON] et al. [30] and aligned with the objectives of our study, albeit focusing on general NLP rather than rumor detection. Expanding on these ideas, [PERSON] et al. [22] proposed a zero-shot cross-lingual and multi-task framework. They introduced a tabular task-language association and the notion of \"coupling models,\" which enhanced the stability and performance during training. Similarly, [PERSON] et al. [23] leveraged English resources to develop emotion detection models for Hindi, employing multilingual embeddings and applying layer freezing techniques to prevent catastrophic forgetting. Focusing on hate speech detection, [PERSON] et al. [79] utilized LaBSE embeddings [80] for zero-shot learning, improving on the SemEval 2019 benchmarks. For rumor detection, [PERSON] et al. [81] implemented a zero-shot learning framework based on MultiFit [82], using a self-training loop for the refinement and evaluation of English and Chinese datasets. Notice that even though this work has similar goals to ours, the authors worked exclusively on the cross-lingual and transfer learning features, but did not use any multi-task approach. More recent work has advanced cross-lingual rumor detection. For example, [PERSON] et al. [83] proposed ACLR-BiGCN, which combines adversarial contrastive learning with graph convolutional networks for detection based on conversations in datasets in English and Chinese. Similarly, [PERSON] et al. [84] introduced MUSCAT, integrating hierarchical conversation representation, multi-head co-attention focused on root tweets, and multilingual pre-trained models, supplementing data through translation and creating the SEAR dataset. The transfer of knowledge from English to Korean was tackled by [PERSON] [85] through a model comprising a news encoder, a language discriminator, and a fake news detector, utilizing adversarial learning and building new datasets for evaluation. Along the same lines, [PERSON] et al. [18] proposed the DES framework for cross-lingual and cross-domain rumor detection, focusing on disentangling emotional and semantic features, adapting them across domains, and employing a joint self-training strategy, evaluated on datasets such as Weibo-COV19 and Twitter-COV19. Recently, [PERSON] et al. [86] introduced a model that utilizes conversation graph structures and an auxiliary self-supervised contrastive task to enhance rumor detection in low-resource and zero-shot scenarios, as tested in the same multilingual datasets. After reviewing related studies, it was observed that the tasks of stance classification and rumor detection have been integrated under a multi-task learning approach. However, as far as is known, such an integration has not been applied to the task of bot detection. Regarding the architectures used, a consensus has been identified concerning the structure of the models, in which shared layers are included across tasks, allowing the learned knowledge to be transferred to support the learning of another task. Task-specific layers are also incorporated, where the learned knowledge is used exclusively for the corresponding task. In addition, a trend has been recognized toward the use of fine-tuning techniques on pre-trained models such as BERT or LaBSE.