Week 8: Translation Platforms, Part 2

Hello everyone!

In this post, I’ll be going into more specifics about Facebook’s and Microsoft’s translation services, and how they differ from Google and each other.

Facebook

Like Google, Facebook also finds back-translation to be extremely helpful in improving low-resource language translation; however, Facebook argues that M4 is not useful for Burmese-English translation in particular, stating that Burmese lacks significant similarities to any high-resource language (Chen et al., 2019). Not only does its language family, Sino-Tibetan, happen to be one of the most structurally diverse families (Sagart et al., 2019), but the only higher-resource Sino-Tibetan languages are Chinese languages (ex: Mandarin, Cantonese, Shanghainese), which are some of the few Sino-Tibetan languages that use SVO word order instead of Burmese’s SOV (Dryer, 2003).

Facebook’s primary parallel datasets are the largest two: the University of Computer Studies, Yangon (UCSY) dataset and the Asian Language Treebank (ALT) dataset (Chen et al., 2019). While Google and Bing haven’t stated that they used the ALT dataset, it’s highly likely that they did as it is the largest publicly available set of parallel Burmese-English sentences. It’s possible that the high scores I’ve been seeing among their translations of ALT data are a partial result of this, which is a limitation I’ll definitely keep in mind in my analysis.

Something unique about Facebook’s translation research is that it considers toxicity (curse words, threats, racial slurs, etc.) and uses a human-generated toxicity detection list to filter out toxic language in training data. This is in order to prevent translations from including toxic language not present in the original text. While not explicitly stated in the paper, I personally think this added emphasis on toxicity detection is related to their previous bad history with hate speech and translation, and although it’s not really part of this project, future research could definitely look at whether this has made Facebook Translate better than other platforms in this regard.

Microsoft

Microsoft also uses a multilingual model similar to M4 – however, instead of training one multilingual model on every language, they use a method called knowledge distillation, where individual “teacher” models are trained on single languages and train a multilingual “student” model to mimic them. Knowledge distillation enhances a model’s accuracy for specific languages, making it more accurate than previous multilingual models (Tan et al., 2019).

Unlike either Google or Facebook, Bing Translate distinguishes between standard, formal, and casual tones (Microsoft Translator Blog, 2016), which could improve translation from English to Burmese, as it can actually take diglossia into account.

In my next post, I’ll be talking about the datasets I’ve used to test these platforms.

Citations:

Chen, P., Shen, J., Le, M., Chaudhary, V., El-Kishky, A., Wenzek, G., Ott, M., & Ranzato, M. “Facebook AI’s WAT19 Myanmar-English Translation Task Submission.” (2019). Proceedings of the 6th Workshop on Asian Translation, pp. 112-122, https://aclanthology.org/D19-5213/.

Costa-jussà, M. R., Cross, J., Çelebi, O., Elbayad, M., Heafield, K., Heffernan, K., Kalbassi, E., Lam, J., Licht, D., Maillard, J., Sun, A., Wang, S., Wenzek, G., Youngblood, A., Akula, B., Barrault, L., Gonzalez, G. M., Hansanti, P., Hoffman, J., Jarrett, S., Sadagopan, K. R., Rowe, D., Spruit, S., Tran, C., Andrews, P., Ayan, N. F., Bhosale, S., Edunov, S., Fan, A., Gao, C., Goswami, V., Guzmán, F., Koehn, P., Mourachko, A., Ropers, C., Saleem, S., Schwenk, H., Wang, J., “No Language Left Behind: Scaling Human-Centered Machine Translation.” (2022). arXiv, https://ai.meta.com/research/publications/no-language-left-behind-scaling-human-centered-machine-translation/.

Dryer, M. “Word order in Sino-Tibetan languages from a typological and geographical perspective.” (2003). The Sino-Tibetan languages.

“Fine-tune and customize your translations in Translator for Bing.” (2016). Microsoft Translator Blog, https://www.microsoft.com/en-us/translator/blog/2016/11/09/fine-tune-and-customize-your-translations-in-translator-for-bing/.

Sagart, L., Jacques, G., Lai, Y., Ryder, R. J., Thouzeau, V., Greenhill, S. J., & List, J. “Dated language phylogenies shed light on the ancestry of Sino-Tibetan.” (2019). Proceedings of the National Academy of Sciences of the United States of America, 116 (21) 10317-10322, https://doi.org/10.1073/pnas.1817972116.

Tan, X., Ren, Y., He, D., Qin, T., Zhao, Z., & Liu, T. “Multilingual neural machine translation with knowledge distillation.” (2019). 7th International Conference on Learning Representations, https://openreview.net/pdf?id=S1gUsoR9YX.