in an effort to increase the representation of low resource languages which a lot of indigenous languages . Many of those languages do not have enough easy available data to train a model using a large dataset. We wanted to develop a translator which would not depend on a lot of data but that would instead learn from the features of a language and words formation.

What it does

Given a word or sentence from a source language (within the list of languages provided), the machine learning algorithm translates the input and outputs the translated result

How we built it

Used Byte-pairing encoding(BPE) for translation method using python. Python to build the machine learning algorithm

Challenges we ran into

Training the model Time constraints when working on the web-app front end and backend processing.

Accomplishments that we're proud of

  • Gaining comprehension of alternative models for translation algorithms.

What we learned

BPE, server-client data transmission using REST, using python for data preparation

What's next for Machine Translation for Low Resource Languages

Improving the training model to better incorporate the use of language-specific features (syntax, grammatical rules, ect) . Developing a more comprehensive webapp that is user-friendly .

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