Counterspeech can be an effective method for battling hateful content on
social media. Automated counterspeech generation can aid in this process.
Generated counterspeech, however, can be viable only when grounded in the
context of topic, audience and sensitivity as these factors influence both the
efficacy and appropriateness. In this work, we propose a novel framework based
on theories of discourse to study the inferential links that connect counter
speeches to the hateful comment. Within this framework, we propose: i) a
taxonomy of counterspeech derived from discourse frameworks, and ii)
discourse-informed prompting strategies for generating contextually-grounded
counterspeech. To construct and validate this framework, we present a process
for collecting an in-the-wild dataset of counterspeech from Reddit. Using this
process, we manually annotate a dataset of 3.9k Reddit comment pairs for the
presence of hatespeech and counterspeech. The positive pairs are annotated for
10 classes in our proposed taxonomy. We annotate these pairs with paraphrased
counterparts to remove offensiveness and first-person references. We show that
by using our dataset and framework, large language models can generate
contextually-grounded counterspeech informed by theories of discourse.
According to our human evaluation, our approaches can act as a safeguard
against critical failures of discourse-agnostic models.

自动对抗言论生成可以帮助社交媒体上打击仇恨内容的有效方法，但生成的对抗言论只有在基于话题、受众和敏感性的背景下才能可行，因为这些因素影响其有效性和适当性。我们提出了一个基于话语理论的新框架，以研究将对抗言论与仇恨评论连接起来的推理链接。在这个框架中，我们提出了：i) 从话语框架中导出的对抗言论分类法，ii) 基于话语理论的上下文化对抗言论生成策略。为了构建和验证这个框架，我们提供了一个从 Reddit 收集实际数据集的过程。利用这个过程，我们手动注释了一个由 3.9k 个 Reddit 评论对组成的数据集，以确定其中是否存在仇恨言论和对抗言论。我们对这些对进行了注释，并提供了重新表述的对应项，以消除冒犯和第一人称参考。我们证明，通过使用我们的数据集和框架，大型语言模型可以生成基于话语理论的上下文化对抗言论。根据我们的人工评估，我们的方法可以作为应对话语不可知模型的重大故障的一种保护措施。

DisCGen: 基于话语的抵制言论生成框架

DisCGen: A Framework for Discourse-Informed Counterspeech Generation

This work summarizes the 2020 ChaLearn Looking at People Fair Face
Recognition and Analysis Challenge and provides a description of the
top-winning solutions and analysis of the results. The aim of the challenge was
to evaluate accuracy and bias in gender and skin colour of submitted algorithms
on the task of 1:1 face verification in the presence of other confounding
attributes. Participants were evaluated using an in-the-wild dataset based on
reannotated IJB-C, further enriched by 12.5K new images and additional labels.
The dataset is not balanced, which simulates a real world scenario where
AI-based models supposed to present fair outcomes are trained and evaluated on
imbalanced data. The challenge attracted 151 participants, who made more than
1.8K submissions in total. The final phase of the challenge attracted 36 active
teams out of which 10 exceeded 0.999 AUC-ROC while achieving very low scores in
the proposed bias metrics. Common strategies by the participants were face
pre-processing, homogenization of data distributions, the use of bias aware
loss functions and ensemble models. The analysis of top-10 teams shows higher
false positive rates (and lower false negative rates) for females with dark
skin tone as well as the potential of eyeglasses and young age to increase the
false positive rates too.

本文总结了 2020 年 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge 的评测结果和优胜解决方案，并提供了对结果的分析。这个比赛的目的是评估提交算法在其他混淆因素存在时的准确性和性别和肤色方面的偏见。参赛者使用一个不平衡的数据集进行了评估，这模拟了 AI 模型在不平衡数据上训练和评估的真实世界情况。最终该比赛吸引了 151 个参与者，36 个团队进入了最后一轮。其中 10 个团队在实现非常低的偏见指标的同时，超过了 0.999 的 AUC-ROC。参赛者们常用的策略包括面部预处理、数据分布的同质化、使用有偏向性的损失函数和集成模型。前 10 名团队的分析还表明，对于肤色较暗的女性，虚假阳性率较高，同时，佩戴眼镜和年龄较小也会增加虚假阳性率。

ECCV 2020 的 FairFace Challenge：分析人脸识别中的偏见

FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition

Synthesising 3D facial motion from speech is a crucial problem manifesting in
a multitude of applications such as computer games and movies. Recently
proposed methods tackle this problem in controlled conditions of speech. In
this paper, we introduce the first methodology for 3D facial motion synthesis
from speech captured in arbitrary recording conditions ("in-the-wild") and
independent of the speaker. For our purposes, we captured 4D sequences of
people uttering 500 words, contained in the Lip Reading Words (LRW) a publicly
available large-scale in-the-wild dataset, and built a set of 3D blendshapes
appropriate for speech. We correlate the 3D shape parameters of the speech
blendshapes to the LRW audio samples by means of a novel time-warping
technique, named Deep Canonical Attentional Warping (DCAW), that can
simultaneously learn hierarchical non-linear representations and a warping path
in an end-to-end manner. We thoroughly evaluate our proposed methods, and show
the ability of a deep learning model to synthesise 3D facial motion in handling
different speakers and continuous speech signals in uncontrolled conditions.

本文采用新的时域扭曲技术 Deep Canonical Attentional Warping (DCAW) 实现了从任意语音录音中综合 3D 面部运动，并经过了充分的评估，可以成功地在处理不同的演讲者和不受控制的语音信号时，合成连续的 3D 面部运动。