Pre-training large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. Although this method has proven to be effective for many domains, it might not always provide desirable benefits. In this paper we study the effects of hateful pre-training on low resource hate speech classification tasks. While previous studies on English language have emphasized its importance, we aim to to augment their observations with some non-obvious insights. We evaluate different variations of tweet based BERT models pre-trained on hateful, non-hateful and mixed subsets of 40M tweet dataset. This evaluation is carried for Indian languages Hindi and Marathi. This paper is an empirical evidence that hateful pre-training is not the best pre-training option for hate speech detection. We show that pre-training on non-hateful text from target domain provides similar or better results. Further, we introduce HindTweetBERT and MahaTweetBERT, the first publicly available BERT models pre-trained on Hindi and Marathi tweets respectively. We show that they provide state-of-the-art performance on hate speech classification tasks. We also release a gold hate speech evaluation benchmark HateEval-Hi and HateEval-Mr consisting of manually labeled 2000 tweets each.

本文研究了针对原本有恶意情绪的文本进行预训练是否为识别恶意言论最佳的预训练方法，结果表明从目标领域的非恶意文本进行预训练可以提供类似或更好的结果，同时介绍了第一批公开开放的印度语Hindi和Marathi的基于BERT的分类模型 HindTweetBERT 和 MahaTweetBERT 。

传递爱而不是仇恨：削弱仇恨预训练对于仇恨言论检测的重要性