As open-ended human-chatbot interaction becomes commonplace, sensitive
content detection gains importance. In this work, we propose a two stage
semi-supervised approach to bootstrap large-scale data for automatic sensitive
language detection from publicly available web resources. We explore various
data selection methods including 1) using a blacklist to rank online discussion
forums by the level of their sensitiveness followed by randomly sampling
utterances and 2) training a weakly supervised model in conjunction with the
blacklist for scoring sentences from online discussion forums to curate a
dataset. Our data collection strategy is flexible and allows the models to
detect implicit sensitive content for which manual annotations may be
difficult. We train models using publicly available annotated datasets as well
as using the proposed large-scale semi-supervised datasets. We evaluate the
performance of all the models on Twitter and Toxic Wikipedia comments testsets
as well as on a manually annotated spoken language dataset collected during a
large scale chatbot competition. Results show that a model trained on this
collected data outperforms the baseline models by a large margin on both
in-domain and out-of-domain testsets, achieving an F1 score of 95.5% on an
out-of-domain testset compared to a score of 75% for models trained on public
datasets. We also showcase that large scale two stage semi-supervision
generalizes well across multiple classes of sensitivities such as hate speech,
racism, sexual and pornographic content, etc. without even providing explicit
labels for these classes, leading to an average recall of 95.5% versus the
models trained using annotated public datasets which achieve an average recall
of 73.2% across seven sensitive classes on out-of-domain testsets.

本文提出了一个两步骤的半监督方法来从公共资源中引导大规模数据，以自动检测敏感语言，展示该方法在公共标注数据集和所提出的半监督数据集上显示出的优异表现和泛化能力，无需为敏感类别提供显式标签，从而在 Twitter 和 Toxic Wikipedia test 数据上实现了高达 95.5％的 F1 得分。