Hateful comments are prevalent on social media platforms. Although tools for
automatically detecting, flagging, and blocking such false, offensive, and
harmful content online have lately matured, such reactive and brute force
methods alone provide short-term and superficial remedies while the
perpetrators persist. With the public availability of large language models
which can generate articulate synthetic and engaging content at scale, there
are concerns about the rapid growth of dissemination of such malicious content
on the web. There is now a need to focus on deeper, long-term solutions that
involve engaging with the human perpetrator behind the source of the content to
change their viewpoint or at least bring down the rhetoric using persuasive
means. To do that, we propose defining and experimenting with controllable
strategies for generating counter-arguments to hateful comments in online
conversations. We experiment with controlling response generation using
features based on (i) argument structure and reasoning-based Walton argument
schemes, (ii) counter-argument speech acts, and (iii) human
characteristics-based qualities such as Big-5 personality traits and human
values. Using automatic and human evaluations, we determine the best
combination of features that generate fluent, argumentative, and logically
sound arguments for countering hate. We further share the developed
computational models for automatically annotating text with such features, and
a silver-standard annotated version of an existing hate speech dialog corpora.

社交媒体平台上存在大量的仇恨评论。我们提出了一种用于生成反驳仇恨评论的可控策略的方法，并通过特征控制响应生成，研究了鼓励长期解决方案的可行性。

整合对抗仇恨言论的策略 —— 利用说服对话

Consolidating Strategies for Countering Hate Speech Using Persuasive  Dialogues

Text generation has received a lot of attention in computational
argumentation research as of recent. A particularly challenging task is the
generation of counter-arguments. So far, approaches primarily focus on
rebutting a given conclusion, yet other ways to counter an argument exist. In
this work, we go beyond previous research by exploring argument undermining,
that is, countering an argument by attacking one of its premises. We
hypothesize that identifying the argument's weak premises is key to effective
countering. Accordingly, we propose a pipeline approach that first assesses the
premises' strength and then generates a counter-argument targeting the weak
ones. On the one hand, both manual and automatic evaluation proves the
importance of identifying weak premises in counter-argument generation. On the
other hand, when considering correctness and content richness, human annotators
favored our approach over state-of-the-art counter-argument generation.

本文研究文本生成在计算辩证研究中的应用，着重探讨对抗性生成的挑战，并提出了攻击一个前提来对抗一个论点的方法，该方法首先评估本体的强度，然后生成一个针对其弱点的反驳性论述，人工和自动评估都证明了找出薄弱的前提对于对抗性生成的重要性。

论据削弱：通过攻击薄弱前提生成反驳论点

Argument Undermining: Counter-Argument Generation by Attacking Weak  Premises

We rely on arguments in our daily lives to deliver our opinions and base them
on evidence, making them more convincing in turn. However, finding and
formulating arguments can be challenging. In this work, we train a language
model for argument generation that can be controlled on a fine-grained level to
generate sentence-level arguments for a given topic, stance, and aspect. We
define argument aspect detection as a necessary method to allow this
fine-granular control and crowdsource a dataset with 5,032 arguments annotated
with aspects. Our evaluation shows that our generation model is able to
generate high-quality, aspect-specific arguments. Moreover, these arguments can
be used to improve the performance of stance detection models via data
augmentation and to generate counter-arguments. We publish all datasets and
code to fine-tune the language model.

本研究利用语言模型进行论点生成，实现针对特定话题、立场和方面的句子级别论点生成，定义论点方面检测作为必要方法，通过数据增强和生成反证来提高立场检测模型的性能。