Manual proof-reading (and data generation) was a big part of it, it's definitely not a glamorous magic process. But as I went through it I could notice patterns and wrote some tools to help.

There's a way to leverage LLMs to help with this if your language is supported (my target wasn't at the time), but I still strongly recommend a manual review part. That's really the secret sauce and no way around it if you're serious about the translation quality of your model.

My goal was a google translate style multilingual translation model, and for that the BART architecture proved ultimately to be better because you benefit from cross-language transfer learning. If your model learns the meaning of "car" in language pair (A, B), and it knows it in language (B, C), then it will perform decently when you ask it to translate between A and C. It compounds very quickly the more you add language pairs.

One big limitation of BART (where LLMs become more attractive) is that it becomes extremely slow for longer sentences, and is less good at understanding and translating complex sentences.

> Any special processing needed for Arabic compared to Latin-script-based

Yes indeed, quite a lot. Especially for Moroccan Arabic which is written in both Arabic and Latin scripts (I made sure to support both and they're aligned in the model's latent space). For this I developed semantic and phonetic embedding models along the way that helped a lot. I am in the process of publishing a paper on the phonetic processing aspect, if you're interested let's stay in touch and I'll let you know when it's out.

But beyond the pre-processing and data pipeline, the model itself didn't need any special treatment besides the tokenizer.

With that said while running it client-side is indeed an option, openly distributing the model is not something I would like to do, at least at this stage. Unlike the bigger projects in the NMT space, including Marian and Bergamot, I don't have any funding, and my monetization plan is to offer inference via API[0].

0: https://api.sawalni.com/docs

For the continuous training itself, yes I simply continue training the model from the last checkpoint (cosine lr scheduler). I am considering doing a full retraining at some point when I collect enough data to compare with this progressive training.

Apologies for the poor links, it takes a lot of time to work on this let alone fully document everything.

0: https://api.sawalni.com/docs#tag/Embeddings

1: https://api.sawalni.com/docs#tag/Language-Identification

The technique makes sense though, but in the training data stage mostly. BART-style translation models already represent concepts in latent space regardless of the input-output language sidestepping English entirely so you have something like:

`source lang —encoded into-> latent space —decoded into—> target lang`

Works great to get translation support for arbitrary language combinations.

On a decent CPU I found the translation to take anywhere between 15-30 seconds depending on the sentence’s length, very unnerving to me as a user.

But it’s definitely worth revisiting that. Thanks!