An exploratory experiment on Hindi, Bengali hate-speech detection and transfer learning using neural networks

01/06/2022
by   Tung Minh Phung, et al.
0

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.

READ FULL TEXT
research
10/04/2018

Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling

Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relati...
research
01/21/2020

Transfer Learning using Neural Ordinary Differential Equations

A concept of using Neural Ordinary Differential Equations(NODE) for Tran...
research
01/03/2019

Weightless Neural Network with Transfer Learning to Detect Distress in Asphalt

The present paper shows a solution to the problem of automatic distress ...
research
02/06/2019

Transfer Learning From Sound Representations For Anger Detection in Speech

In this work, we train fully convolutional networks to detect anger in s...
research
06/10/2019

Char-RNN for Word Stress Detection in East Slavic Languages

We explore how well a sequence labeling approach, namely, recurrent neur...
research
06/24/2019

Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia

Despite continuing medical advances, the rate of newborn morbidity and m...

Please sign up or login with your details

Forgot password? Click here to reset