Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model

S. Alathur, N. Chetty, R. R. Pai, V. Kumar, and S. Dhelim, “Hate and False Metaphors: Implications to Emerging E-Participation Environment,” Future Internet , vol. 14, no. 11, p. 314, oct 2022. [Online]. Available: https://www.mdpi.com/1999-5903/14/11/314

1999

Earlier work this paper cites.

T. B. Brown, B. Mann, N. Ryder et al. , “Language models are few-shot learners,” in Advances in Neural Information Processing Systems , vol. 33, 2020, pp. 1877–1901. [Online]. Available: https://arxiv.org/abs/2005.14165

Original

2005

Earlier work this paper cites.

R. Nassar, A. Helmy, and N. Ramadan, “Depression corpus of arabic tweets,” 12 2022. [Online]. Available: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YHMYEQ

Original

2016

Earlier work this paper cites.

M. De Choudhury and E. Kiciman, “The language of social support in social media and its effect on suicidal ideation risk,” Proceedings of the … International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media , vol. 2017, p. 32, 2017. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5565730/

2017

Earlier work this paper cites.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, L. Gomez, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , vol. 30, 2017

2017

Earlier work this paper cites.

C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proceedings of the 34th International Conference on Machine Learning . PMLR, 7 2017, pp. 1126–1135. [Online]. Available: https://proceedings.mlr.press/v70/finn17a.html

2017

Earlier work this paper cites.

J. Gordon, J. Bronskill, S. Nowozin, M. Bauer, and R. E. Turner, “Meta-learning probabilistic inference for prediction,” in 7th International Conference on Learning Representations (ICLR 2019) , 5 2018. [Online]. Available: https://arxiv.org/abs/1805.09921v4

Original

2018

Earlier work this paper cites.

L. Coello-Guilarte, R. M. Ortega-Mendoza, L. Villaseñor-Pineda, and M. M. y Gómez, “Crosslingual depression detection in twitter using bilingual word alignments,” in Lecture Notes in Computer Science , vol. 11696 LNCS. Springer, 2019, pp. 49–61. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-030-28577-7_2

2019

Earlier work this paper cites.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics . Association for Computational Linguistics, 2019, pp. 4171–4186. [Online]. Available: https://aclanthology.org/N19-1423

2019

Earlier work this paper cites.

H. Ning, S. Dhelim, and N. Aung, “PersoNet: Friend Recommendation System Based on Big-Five Personality Traits and Hybrid Filtering,” IEEE Transactions on Computational Social Systems , vol. 6, no. 3, pp. 394–402, jun 2019. [Online]. Available: https://ieeexplore.ieee.org/document/8675299/

2019

Earlier work this paper cites.

E. Turcan and K. McKeown, “Dreaddit: A reddit dataset for stress analysis in social media,” in Proceedings of the 10th International Workshop on Health Text Mining and Information Analysis (LOUHI@EMNLP) . Association for Computational Linguistics (ACL), 2019, pp. 97–107. [Online]. Available: https://aclanthology.org/d19-6213

2019

Earlier work this paper cites.

“World health statistics,” World Health Organization, March 2020. [Online]. Available: https://www.who.int/data/gho/publications/world-health-statistics

2020

Earlier work this paper cites.

M. Popel, M. Tomkova, J. Tomek, Łukasz Kaiser, J. Uszkoreit, O. Bojar, and Z. Žabokrtský, “Transforming machine translation: A deep learning system reaches news translation quality comparable to human professionals,” Nature Communications , vol. 11, no. 1, p. 4381, 2020. [Online]. Available: https://www.nature.com/articles/s41467-020-18073-9

2020

Earlier work this paper cites.

P. Joshi, S. Santy, A. Budhiraja, K. Bali, and M. Choudhury, “The state and fate of linguistic diversity and inclusion in the nlp world,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Association for Computational Linguistics, 2020, pp. 6282–6293. [Online]. Available: https://aclanthology.org/2020.acl-main.560

2020

Earlier work this paper cites.

F. Nooralahzadeh, J. Bjerva, G. Bekoulis, and I. Augenstein, “Zero-shot cross-lingual transfer with meta learning,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Association for Computational Linguistics, 2020, pp. 4547–4562. [Online]. Available: https://aclanthology.org/2020.emnlp-main.368

2020

Earlier work this paper cites.

S. Dhelim, N. Aung, and H. Ning, “Mining user interest based on personality-aware hybrid filtering in social networks,” Knowledge-Based Systems , vol. 206, p. 106227, oct 2020. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0950705120304354

2020

Earlier work this paper cites.

G. L. Martin, M. E. Mswahili, and Y.-S. Jeong, “Sentiment classification in swahili language using multilingual bert,” 4 2021. [Online]. Available: https://arxiv.org/abs/2104.09006v1

Original

2021

Earlier work this paper cites.

R. Cattoni, M. A. D. Gangi, L. Bentivogli, M. Negri, and M. Turchi, “Must-c: A multilingual corpus for end-to-end speech translation,” Computer Speech & Language , vol. 66, p. 101155, 2021

2021

Earlier work this paper cites.

J. W. Rae, S. Borgeaud, T. Cai et al. , “Scaling language models: Methods, analysis & insights from training gopher,” 12 2021, preprint on arXiv. [Online]. Available: https://arxiv.org/abs/2112.11446v2

Original

2021

Cited alongside, same era.

A. Ghafoor, A. S. Imran, S. M. Daudpota, Z. Kastrati, Abdullah, R. Batra, and M. A. Wani, “The impact of translating resource-rich datasets to low-resource languages through multi-lingual text processing,” IEEE Access , vol. 9, pp. 124 478–124 490, 2021

2021

Cited alongside, same era.

S. Dhelim, H. Ning, N. Aung, R. Huang, and J. Ma, “Personality-Aware Product Recommendation System Based on User Interests Mining and Metapath Discovery,” IEEE Transactions on Computational Social Systems , vol. 8, no. 1, pp. 86–98, feb 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9269396/

2021

Cited alongside, same era.

S. Dhelim, H. Ning, and N. Aung, “ComPath: User Interest Mining in Heterogeneous Signed Social Networks for Internet of People,” IEEE Internet of Things Journal , vol. 8, no. 8, pp. 7024–7035, apr 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9253614/

S. Dhelim, L. Chen, H. Ning, and C. Nugent, “Artificial intelligence for suicide assessment using Audiovisual Cues: a review,” Artificial Intelligence Review , vol. 56, no. 6, pp. 5591–5618, jun 2023. [Online]. Available: https://link.springer.com/10.1007/s10462-022-10290-6

2023

Later among the works it cites.

X. Yang, Y. Li, X. Zhang, H. Chen, and W. Cheng, “Exploring the limits of chatgpt for query or aspect-based text summarization,” 2 2023. [Online]. Available: https://arxiv.org/abs/2302.08081v1

Original

2023

Later among the works it cites.

B. Lamichhane, “Evaluation of chatgpt for nlp-based mental health applications,” 3 2023. [Online]. Available: https://arxiv.org/abs/2303.15727v1

Original

2023

Later among the works it cites.

H. Ghanadian, I. Nejadgholi, and H. Al Osman, “Chatgpt for suicide risk assessment on social media: Quantitative evaluation of model performance, potentials and limitations,” 6 2023. [Online]. Available: https://arxiv.org/abs/2306.09390v1

Original

2023