Toxic language detection systems often falsely flag text that contains
minority group mentions as toxic, as those groups are often the targets of
online hate. Such over-reliance on spurious correlations also causes systems to
struggle with detecting implicitly toxic language. To help mitigate these
issues, we create ToxiGen, a new large-scale and machine-generated dataset of
274k toxic and benign statements about 13 minority groups. We develop a
demonstration-based prompting framework and an adversarial
classifier-in-the-loop decoding method to generate subtly toxic and benign text
with a massive pretrained language model. Controlling machine generation in
this way allows ToxiGen to cover implicitly toxic text at a larger scale, and
about more demographic groups, than previous resources of human-written text.
We conduct a human evaluation on a challenging subset of ToxiGen and find that
annotators struggle to distinguish machine-generated text from human-written
language. We also find that 94.5% of toxic examples are labeled as hate speech
by human annotators. Using three publicly-available datasets, we show that
finetuning a toxicity classifier on our data improves its performance on
human-written data substantially. We also demonstrate that ToxiGen can be used
to fight machine-generated toxicity as finetuning improves the classifier
significantly on our evaluation subset. Our code and data can be found at
this https URL.

本论文介绍了 ToxiGen，一个新的大规模自动生成的 274k 毒性和良性陈述数据集，用于检测涉及 13 个少数群体的文本。通过使用基于展示的提示框架和诱导循环解码方法来生成微妙的毒性和良性文本，ToxiGen 能够覆盖范围更广的暗含毒性文本，包括更多样化的人口群体。与此同时，研究者通过人类评估表明，94.5％的毒性示例被人类标注者标记为仇恨言论。合理的数据利用对文本分类器的提高有积极的作用。

ToxiGen：一个大规模机器生成的用于对抗和隐式仇恨言论检测的数据集

ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection

We introduce an approach to multilingual speech synthesis which uses the
meta-learning concept of contextual parameter generation and produces
natural-sounding multilingual speech using more languages and less training
data than previous approaches. Our model is based on Tacotron 2 with a fully
convolutional input text encoder whose weights are predicted by a separate
parameter generator network. To boost voice cloning, the model uses an
adversarial speaker classifier with a gradient reversal layer that removes
speaker-specific information from the encoder.
We arranged two experiments to compare our model with baselines using various
levels of cross-lingual parameter sharing, in order to evaluate: (1) stability
and performance when training on low amounts of data, (2) pronunciation
accuracy and voice quality of code-switching synthesis. For training, we used
the CSS10 dataset and our new small dataset based on Common Voice recordings in
five languages. Our model is shown to effectively share information across
languages and according to a subjective evaluation test, it produces more
natural and accurate code-switching speech than the baselines.

本文介绍一种多语言语音合成方法，该方法使用上下文参数生成的元学习概念，使用更少的训练数据和更多的语言产生自然音质的多语言语音；其模型使用 Tacotron 2 和完全卷积输入文本编码器，在训练中使用 CSS10 数据集和新的基于 5 种语言的通用语音数据集，同时使用对抗分类器和梯度反转层增强语音克隆。实验结果表明，该模型跨语言信息共享效果良好，在 “代码切换” 语音合成中表现更自然、准确。