HABERTOR: An Efficient and Effective Deep Hatespeech Detector

TL;DR

HABERTOR is a novel deep hatespeech detection model that improves accuracy, efficiency, and robustness by customizing BERT with quaternion components, multi-source ensemble heads, and adversarial training on large-scale datasets.

Contribution

It introduces a BERT-based hatespeech detector with quaternion factorization, multi-source ensemble heads, and adversarial training, achieving superior performance and efficiency over existing methods.

Findings

Outperforms 15 state-of-the-art hatespeech detection methods.

4-5 times faster training and inference than BERT.

Uses less than 1% of the words for pre-training.

Abstract

We present our HABERTOR model for detecting hatespeech in large scale user-generated content. Inspired by the recent success of the BERT model, we propose several modifications to BERT to enhance the performance on the downstream hatespeech classification task. HABERTOR inherits BERT's architecture, but is different in four aspects: (i) it generates its own vocabularies and is pre-trained from the scratch using the largest scale hatespeech dataset; (ii) it consists of Quaternion-based factorized components, resulting in a much smaller number of parameters, faster training and inferencing, as well as less memory usage; (iii) it uses our proposed multi-source ensemble heads with a pooling layer for separate input sources, to further enhance its effectiveness; and (iv) it uses a regularized adversarial training with our proposed fine-grained and adaptive noise magnitude to enhance its robustness. Through experiments on the large-scale real-world hatespeech dataset with 1.4M annotated comments, we show that HABERTOR works better than 15 state-of-the-art hatespeech detection methods, including fine-tuning Language Models. In particular, comparing with BERT, our HABERTOR is 4~5 times faster in the training/inferencing phase, uses less than 1/3 of the memory, and has better performance, even though we pre-train it by using less than 1% of the number of words. Our generalizability analysis shows that HABERTOR transfers well to other unseen hatespeech datasets and is a more efficient and effective alternative to BERT for the hatespeech classification.