An exploratory experiment on Hindi, Bengali hate-speech detection and transfer learning using neural networks
This work addresses hate speech detection for low-resource languages like Hindi and Bengali, though it appears incremental in scope.
The researchers trained a neural network to detect hate speech in Hindi and Bengali, exploring transfer learning between these similar languages, and achieved results comparable to more expensive models despite limited computational resources.
This work presents our approach to train a neural network to detect hate-speech texts in Hindi and Bengali. We also explore how transfer learning can be applied to learning these languages, given that they have the same origin and thus, are similar to some extend. Even though the whole experiment was conducted with low computational power, the obtained result is comparable to the results of other, more expensive, models. Furthermore, since the training data in use is relatively small and the two languages are almost entirely unknown to us, this work can be generalized as an effort to demystify lost or alien languages that no human is capable of understanding.