Week 7: Translation Platforms, Part 1

Hello everyone!

In this blog post (and the next), I’ll be going into more specifics about the three translation platforms I’m evaluating in my project: Google, Facebook, and Microsoft, and how they’ve been impacted by advancements in machine learning as a whole.

Google

Between 2019 and 2020, machine learning improvements enabled Google Translate to implement several features that led to significant increases in BLEU score across all 100+ languages supported on the platform, particularly low-resource languages (Caswell & Liang, 2020). I’ll mostly be focusing on updates that were aimed at low-resource languages. Since low-resource languages definitionally lack ample parallel data, which is what models are typically trained on to translate high-resource languages, these methods aim to use more abundant monolingual data to train translation models.

One such method is back-translation. Back-translation takes a human-written sentence in the target language and has a machine translation model generate a translation for it, creating synthetic parallel data that can then be used to train other models. In order to prevent models from learning from their own mistakes, lower-quality synthetic parallel sentences are filtered out using automated scoring systems (Khatri & Bhattacharyya, 2020) or the model’s own confidence ratings (Wang et al., 2019). Back-translation primarily leads to gains in fluency (Caswell & Liang, 2020).

Another is M4 (Massively Multilingual, Massive Neural Machine Translation). M4 is the usage of a single model to translate between every supported language and one target language, typically English. Not only is this much more cost-effective and efficient, but it also improves translation quality for low-resource languages because the model can use its findings from related languages with far more resources (Arivazhagan et al., 2019). For instance, Yiddish, a low-resource Germanic language, can be translated more accurately because the model has access to data from higher-resource Germanic languages such as German and Dutch (Caswell & Liang, 2020). M4 also enables zero-shot machine translation, which is when models are able to translate between two languages with no parallel data. Although this doesn’t pertain to Burmese-English translation, it’s enabled Google Translate to add 110 new languages in 2024 alone (Caswell, 2024).

However, Google Translate’s newer models have been slightly more prone to hallucinations than previous models (Caswell & Liang, 2020). Hallucinations can occur when models attempt to come up with meaningful translations from meaningless input, or simply add completely unrelated information. While I haven’t experimented much with genuinely nonsensical data, I’ve observed a few hallucinations in my social media data, which I’ll talk about soon.

I ended up saying a lot more about Google than I originally anticipated – my next blog post will be a (hopefully shorter) description of Facebook’s and Microsoft’s translation models.

Citations:

Arivazhagan, N., Bapna, A., Firat, O., Lepikhin, D., Johnson, M., Krikun, M., Chen, M. X., Cao, Y., Foster, G., Cherry, C., Macherey, W., Chen, Z., & Wu, Y. (2019). “Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges.” arXiv, https://arxiv.org/abs/1907.05019.

Caswell, I. “110 new languages are coming to Google Translate.” (2024). Google, https://blog.google/products/translate/google-translate-new-languages-2024/.

Caswell, I. & Liang, B. “Recent Advances in Google Translate.” (2020). Google Research, https://research.google/blog/recent-advances-in-google-translate/.

Dale, D., Voita, E., Barrault, L., Costa-jussà, M. R. “Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better.” Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers, pp. 36-50, https://aclanthology.org/2023.acl-long.3.pdf.

Khatri, J. & Bhattacharyya, P. “Filtering Back-Translated Data in Unsupervised Neural Machine Translation.” (2020). Proceedings of the 28th International Conference on Computational Linguistics, pp. 4334-4339, https://aclanthology.org/2020.coling-main.383.pdf.

Wang, S., Liu, Y., Wang, C., Luan, H., & Sun, M. “Improving Back-Translation with Uncertainty-based Confidence Estimation.” (2019). Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 791-802, https://aclanthology.org/D19-1073.pdf.