A Simple Yet Promising Machine Translation Pipeline for Low-Resource Languages

#translation #nlp #machine-learning

Machine translation for languages with limited resources is a difficult challenge. Here’s a straightforward pipeline that could yield some interesting results:

1. Accuracy First: Start with an interlinear-like, literalistic rendering. This step uses statistical glossing or high-granularity predictions to create a word-for-word translation. It’s not pretty, but it’s “accurate” in the sense that it is a representation of the source text.

2. Naturalness Post-Edit: Here’s where it gets interesting. Take your literal translation and try to iteratively make it sound natural:

   • Gather as much unstructured text in the target language as you can.
   • Measure the likelihood of your output from step 1 using different slices of this data (think sliding window n-grams or similar techniques). (This is kind of like measuring perplexity of a model.)
   • Systematically swap and rearrange parts of the translation.
   • Rank the different versions based on their ‘naturalness’ (as defined by the likelihood measurements).

This approach seems deceptively simple, but it could be quite promising. By separating the accuracy and naturalness concerns, we’re able to leverage the strengths of both statistical and corpus-based methods.

The beauty of this pipeline is its potential to work with limited resources. Even if we don’t have parallel corpora (expensive to get), or extensive training data (requires model fine-tuning, quickly gets out of date, and likely will be inaccurate until some critical mass of data is reached), we can still produce a reasonable translation by focusing first on word-level accuracy and then on phrase-level naturalness.

You could also add additional steps to the pipeline, such as:

• Back-Translation: Translate the output of the previous step (now in the target language) back to the source language for comparison.
• Gloss at Lower Granularity: Use the output from the previous step and attempt to re-gloss the text at a lower granularity (e.g., complete phrases).

Of course, there are challenges to consider. I suspect such an approach would work best for languages that are similar to the source language, perhaps from the same language family. The potential here lies in the use of more traditional, statistical approaches to machine translation, which are typically much faster and can be iterated many times with minimal resources and more easily measured for success.