Aindra T. 2025 | BASIS Independent Silicon Valley
- Project Title: Burmese-English Machine Translation Accuracy
- BASIS Independent Advisor: Ms. Canevaro
- Internship Location: Frontiir
- Onsite Mentor: Phyo Kyaw, Director of Engineering at Frontiir
The vast majority of my research will be done completely digitally. Before beginning my actual project, I will conduct more internet research on more technical details of machine translation as well as the algorithms behind the evaluation metrics I discussed earlier, such as BLEU and RIBES. The project itself will involve translating English and Burmese sentences on different platforms and then evaluating their accuracy using existing metrics. Depending on time, I may also ask bilingual human judges to rate translations. My final product will be a research paper explaining the results.
My Posts
Week 10: Preliminary Findings — Social Media
May 11, 2025
Hello everyone! In this post, I’ll be sharing some of my observations from my social media dataset. Expected Results All three platforms seem to struggle with incomplete and grammatically incorrect sentences the most. Accuracy as a whole is noticeably lower than the ALT dataset. The ALT dataset had few hallucinations, but with social media, they […]
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Week 9: Datasets
May 11, 2025
Hello everyone! In this post, I’ll be talking about the datasets I’ve used for testing. (This is a post I probably should’ve made much earlier, oops.) The Asian Language Treebank (ALT) Parallel Corpus The ALT project was initiated by the Advanced Speech Translation Research and Development Promotion Center in Japan in order to promote NLP […]
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Week 8: Translation Platforms, Part 2
May 11, 2025
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, […]
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Week 7: Translation Platforms, Part 1
May 11, 2025
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 […]
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