Call Larisa Ivanovna: Code-Switching Fools Multilingual NLU Models

Author:

Birshert Alexey,Artemova Ekaterina

Publisher

Springer International Publishing

Reference46 articles.

1. Abdul-Mageed, M., Lakshmanan, L.V.: Exploring text-to-text transformers for English to Hinglish machine translation with synthetic code-mixing. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 36–46 (2021)

2. Aguilar, G., Kar, S., Solorio, T.: LinCE: A centralized benchmark for linguistic code-switching evaluation. In: Proceedings of The 12th Language Resources and Evaluation Conference. pp. 1803–1813. European Language Resources Association, Marseille, France (2020). https://www.aclweb.org/anthology/2020.lrec-1.223

3. Chen, Q., Zhuo, Z., Wang, W.: BERT for joint intent classification and slot filling (2019)

4. Cheng, M., Yi, J., Chen, P.Y., Zhang, H., Hsieh, C.J.: Seq2sick: Evaluating the robustness of sequence-to-sequence models with adversarial examples. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3601–3608 (2020)

5. Conneau, A.: Unsupervised cross-lingual representation learning at scale(2019). arXiv preprint arXiv:1911.02116

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