Bringing Comorian Language to Life Through Tech

Africa is home to thousands of languages, each with its own unique charm and history. Some languages, like Swahili, are well-supported with resources for technology development, while others are not so lucky. Comorian, a language spoken in the Comoros islands with four different dialects, is one of those underrepresented languages. It's like having a fancy smartphone but not being able to find any apps to use.

This article explores how we can help Comorian catch up in the language tech race using a trick called Transfer Learning. Think of it as giving a little boost to a buddy who's not as fast on the track, thanks to someone else's good training. Let’s take a closer look at the beautiful, diverse world of Comorian and what we’re doing to bring it into the modern age.

#What is Comorian?

Comorian consists of four main dialects: ShiNgazidja, ShiMwali, ShiNdzuani, and ShiMaore. Each dialect is tied to one of the islands in the Comoros archipelago. Communication can be tricky among the dialects. For example, someone from the northern part of Ngazidja might scratch their head in confusion when hearing someone from the south. It’s a bit like speaking the same language but having different accents or regional slang.

Imagine someone saying “egg” — in one dialect, it’s “djwai,” and in another, “dzundzu.” Ever heard of “mayayi”? That's the plural. Each island has its own special twist, making Comorian as colorful as a box of crayons. However, this diversity presents a challenge for creating tech solutions since it’s tough to gather data that truly represents all variations.

#The Challenge of Limited Resources

Creating natural language processing (NLP) technology for Comorian is like trying to bake a cake with only half the ingredients. While there’s plenty of flour and sugar for Swahili, Comorian is short on the essential ingredients. Without enough data, developing effective NLP applications becomes a huge mountain to climb.

So, how do we build a cake when some of the ingredients are missing? One approach is to use a well-resourced language like Swahili to help fill in the gaps for Comorian. That’s where transfer learning comes into play, acting as a bridge between Swahili and Comorian. Think of it as having a friend who knows how to cook share their recipe and techniques with you.

#Transfer Learning: The Recipe for Success

Transfer learning lets us use the skills and knowledge gained from one language (in this case, Swahili) and apply it to another language that needs a helping hand. It’s like using a successful workout plan to get in shape for a different sport.

In our case, we mix data from both languages to create a robust dataset. This involves taking Swahili text and picking out elements closest to Comorian. By gathering data this way, we can efficiently teach computers how to understand and generate Comorian, even with limited resources.

#Building the Datasets

To create a working dataset, we combine Swahili content with local Comorian data. Cleaning the data is like washing your fruits and vegetables before cooking; it ensures that we only use the best bits. Every word counts, especially when you have a limited supply.

We also dive into audio data to help build systems for Automatic Speech Recognition (ASR) and Machine Translation (MT). This means we’re not just teaching computers how to read Comorian but also how to listen.

#How We Tested Our Ideas

To check how well our approach works, we created two main use cases: ASR and MT.

#Automatic Speech Recognition (ASR)

For ASR, we wanted to train a model that recognizes spoken Comorian. We utilized a mix of Swahili audio recordings while filtering for content that included Comorian words. It’s a bit like collecting music from different genres but making sure your playlist has your favorite songs included.

After processing the audio, we ended up with around four hours of labeled data. It’s a decent amount for starting out, but there’s always room for more!

#Machine Translation (MT)

Next up is MT, which helps in translating Comorian to other languages, such as English or French. We used the previous datasets and translated sentences from Swahili to English, resulting in a final collection of 30,000 translated sentences along with the original Comorian data. That’s quite a bit of text to chew on — enough to keep a translator busy!

#The Importance of Lexical Distances

To understand how close Swahili and Comorian really are, we calculated lexical distances. This means figuring out how similar or different words are in both languages. If you think of language as a family tree, the closer the words are on the tree, the more they share.

Using the Swadesh list, a compilation of common words across various languages, we found out that Swahili and Comorian are indeed quite close, just like cousins who share a mutual uncle. This closeness is vital because it strengthens our belief that transfer learning will work.

#Initial Results

After running our models, we got some promising results!

#Machine Translation Results

Our machine translation model had ROUGE scores that indicated it was doing a decent job of translating Comorian. The results show that the model can capture important sentence structures and vocabulary, which is exciting for the future of Comorian language technology.

#Automatic Speech Recognition Results

In terms of ASR, our model also performed well, achieving reasonable accuracy with its output. Although the word error rate (WER) and character error rate (CER) could use some work, the results signal that we’re headed in the right direction.

#Broader Applications

Our efforts to improve Comorian technology can have far-reaching consequences. By making it easier for people to communicate in Comorian, we can enhance tourist experiences in the Comoros, where the number of visitors has been growing in recent years. Imagine tourists asking for directions or ordering food in perfect Comorian, making their stay more enjoyable and authentic!

Furthermore, our work goes beyond just language processing. It’s about preserving the rich cultural heritage of the Comoros in the digital world. If we can equip local communities with technology, they can share their stories and keep their language alive for future generations.

#Conclusion: A Bright Future Ahead

The journey to develop NLP solutions for Comorian may be challenging, but the benefits are clear. In a world where many languages struggle to find their place in technology, transfer learning offers a promising path. By leveraging the resources of Swahili, we can breathe life into Comorian, ensuring it has a fair shot at success in the modern world.

So, while we may not have the same cake ingredients as Swahili, we can still bake a delicious treat for the Comorian people. With time, effort, and a sprinkle of creativity, the Comorian language can thrive alongside its more resourceful peers, proving that every language has a right to be heard in the digital age.