Counter-argument generation -- a captivating area in computational
linguistics -- seeks to craft statements that offer opposing views. While most
research has ventured into paragraph-level generation, sentence-level
counter-argument generation beckons with its unique constraints and
brevity-focused challenges. Furthermore, the diverse nature of
counter-arguments poses challenges for evaluating model performance solely
based on n-gram-based metrics. In this paper, we present the ArgTersely
benchmark for sentence-level counter-argument generation, drawing from a
manually annotated dataset from the ChangeMyView debate forum. We also propose
Arg-LlaMA for generating high-quality counter-argument. For better evaluation,
we trained a BERT-based evaluator Arg-Judge with human preference data. We
conducted comparative experiments involving various baselines such as LlaMA,
Alpaca, GPT-3, and others. The results show the competitiveness of our proposed
framework and evaluator in counter-argument generation tasks. Code and data are
available at this https URL

本研究提出了 ArgTersely 基准和 Arg-LlaMA 模型，使用基于 BERT 的 Arg-Judge 评估器进行了高质量句级反驳生成任务的实验，在与其他基线模型比较中展示了该框架和评估器的竞争力。

与我简明辩论：走向句级反驳生成

Argue with Me Tersely: Towards Sentence-Level Counter-Argument  Generation

In real-world debates, the most common way to counter an argument is to
reason against its main point, that is, its conclusion. Existing work on the
automatic generation of natural language counter-arguments does not address the
relation to the conclusion, possibly because many arguments leave their
conclusion implicit. In this paper, we hypothesize that the key to effective
counter-argument generation is to explicitly model the argument's conclusion
and to ensure that the stance of the generated counter is opposite to that
conclusion. In particular, we propose a multitask approach that jointly learns
to generate both the conclusion and the counter of an input argument. The
approach employs a stance-based ranking component that selects the counter from
a diverse set of generated candidates whose stance best opposes the generated
conclusion. In both automatic and manual evaluation, we provide evidence that
our approach generates more relevant and stance-adhering counters than strong
baselines.

本文研究如何利用多任务学习来生成经过反驳的论点，并提出了一个基于立场分类的排名组件来选择最优的反驳。评估结果表明，该方法比强基线生成的反驳更相关且立场更明确。

基于结论的对抗性论点生成

Conclusion-based Counter-Argument Generation

Automatic argument generation is an appealing but challenging task. In this
paper, we study the specific problem of counter-argument generation, and
present a novel framework, CANDELA. It consists of a powerful retrieval system
and a novel two-step generation model, where a text planning decoder first
decides on the main talking points and a proper language style for each
sentence, then a content realization decoder reflects the decisions and
constructs an informative paragraph-level argument. Furthermore, our generation
model is empowered by a retrieval system indexed with 12 million articles
collected from Wikipedia and popular English news media, which provides access
to high-quality content with diversity. Automatic evaluation on a large-scale
dataset collected from Reddit shows that our model yields significantly higher
BLEU, ROUGE, and METEOR scores than the state-of-the-art and non-trivial
comparisons. Human evaluation further indicates that our system arguments are
more appropriate for refutation and richer in content.

本文介绍了一个新的框架 CANDELA，用来解决针对特定问题的对抗论证自动生成，它包含了一个强大的检索系统和一个新型的双步骤生成模型，通过索引 1200 万文章并使用自然语言处理，使得自动生成的对抗论证比以往更为适当和丰富。经过自动评估和人工评估，该模型均取得了相比现有技术的更高 BLEU、ROUGE 和 METEOR 语言分数。