[10] Mohaddeseh Bastan, Mahnaz Koupae, Youngseo Son, Richard Sicoli, and Niranjan Balasubramanian. Author's sentiment prediction. In Proceedings of the 28th International Conference on Computational Linguistics, pages 604-615, Barcelona, Spain (Online), December 2020. International Committee on Computational Linguistics. doi: 10.18653/v1/2020.coling-main.52. URL https://aclanthology.org/2020.coling-main.52.
[11] Vuk Batanović, Boško Nikolić, and Milan Milosavljevic. Reliable baselines for sentiment analysis in resource-limited languages: The Serbian movie review dataset. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 2688-2696, Porto.roz, Slovenia, May 2016. European Language Resources Association (ELRA). URL https://aclanthology.org/L16-1427.
[12] Vuk Batanovic, Miloš Cvetanović, and Boško Nikolić. A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts. PLOS ONE, 15(11):1-30, 11 2020. doi: 10.1371/journal.pone.0242050. URL https://doi.org/10.1371/journal.pone.0242050.
[13] Henrico Brum and Maria das Graças Volpe Nunes. Building a sentiment corpus of tweets in Brazilian Portuguese. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, May 2018. European Language Resources Association (ELRA). URL https://aclanthology.org/L18-1658.
[14] Joze Bučar, Martin Žnidaršić, and Janez Povh. Annotated news corpora and a lexicon for sentiment analysis in slovene. *Language Resources and Evaluation*, 52(3):895-919, September 2018. doi: 10.1007/s10579-018-9413-3.
[15] Lyle Campbell. Ethnologue: Languages of the world, 2008.
[16] Ethem F. Can, Aysu Ezen-Can, and Fazli Can. Multilingual sentiment analysis: An RNN-based framework for limited data. Computing Research Repository, arXiv:1806.04511, 2018. URL http://arxiv.org/abs/1806.04511. Version 1.
[17] Emile Chapuis, Pierre Colombo, Matteo Manica, Matthieu Labeau, and Chloé Clavel. Hierarchical pre-training for sequence labelling in spoken dialog. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2636-2648, Online, November 2020. Association for Computational Linguistics. doi: 10.18653/v1/2020.findings-emnlp.239. URL https://aclanthology.org/2020 findings-emnlp.239.
[18] Yanqing Chen and Steven Skiena. Building sentiment lexicons for all major languages. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 383-389, 2014.
[19] Mark Cieliebak, Jan Milan Deriu, Dominic Egger, and Fatih Uzdilli. A Twitter corpus and benchmark resources for German sentiment analysis. In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, pages 45-51, Valencia, Spain, April 2017. Association for Computational Linguistics. doi: 10.18653/v1/W17-1106. URL https://aclanthology.org/W17-1106.
[20] Alexis Conneau and Guillaume Lample. Cross-lingual language model pretraining. In Proceedings of the 33rd International Conference on Neural Information Processing Systems, Red Hook, NY, USA, 2019. Curran Associates Inc. URL https://dl.acm.org/doi/10.5555/3454287.3454921.
[21] Alexis Conneau, Rudy Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, and Veselin Stoyanov. XNLI: Evaluating cross-lingual sentence representations. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2475–2485, Brussels, Belgium, October-November 2018. Association for Computational Linguistics. doi: 10.18653/v1/D18-1269. URL https://aclanthology.org/D18-1269.
[22] Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th Annual