Editing human-written text has become a standard use case of large language models (LLMs), for example, to make one's arguments more appropriate for a discussion.Comparing human to LLM-generated edits, however, we observe a mismatch in editing strategies: While LLMs often perform multiple scattered edits and tend to change meaning notably, humans rather encapsulate dependent changes in selfcontained, meaning-preserving edits.In this paper, we present a reinforcement learning approach that teaches LLMs human-like editing to improve the appropriateness of arguments.Our approach produces self-contained sentence-level edit suggestions that can be accepted or rejected independently.We train the approach using group relative policy optimization with a multi-component reward function that jointly optimizes edit-level semantic similarity, fluency, and pattern conformity as well as argument-level appropriateness.In automatic and human evaluation, it outperforms competitive baselines and the state of the art in humanlike editing, with multi-round editing achieving appropriateness close to full rewriting.

Timon Ziegenbein, Gabriella Skitalinskaya, Alireza Bayat Makou, Henning Wachsmuth. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, writing education, and the like. A critical task in any such application is the assessment of an argument’s quality - but it is also particularly challenging. In this position paper, we start from a brief survey of argument quality research, where we identify the diversity of quality notions and the subjectiveness of their perception as the main hurdles towards substantial progress on argument quality assessment. We argue that the capabilities of instruction-following large language models (LLMs) to leverage knowledge across contexts enable a much more reliable assessment. Rather than just fine-tuning LLMs towards leaderboard chasing on assessment tasks, they need to be instructed systematically with argumentation theories and scenarios as well as with ways to solve argument-related problems. We discuss the real-world opportunities and ethical issues emerging thereby.

Online discussion moderators must make adhoc decisions about whether the contributions of discussion participants are appropriate or should be removed to maintain civility.Existing research on offensive language and the resulting tools cover only one aspect among many involved in such decisions.The question of what is considered appropriate in a controversial discussion has not yet been systematically addressed.In this paper, we operationalize appropriate language in argumentation for the first time.In particular, we model appropriateness through the absence of flaws, grounded in research on argument quality assessment, especially in aspects from rhetoric.From these, we derive a new taxonomy of 14 dimensions that determine inappropriate language in online discussions.Building on three argument quality corpora, we then create a corpus of 2191 arguments annotated for the 14 dimensions.Empirical analyses support that the taxonomy covers the concept of appropriateness comprehensively, showing several plausible correlations with argument quality dimensions.Moreover, results of baseline approaches to assessing appropriateness suggest that all dimensions can be modeled computationally on the corpus.

Online discussions on controversial topics with many participants frequently include hundreds of arguments that cover different framings of the topic.But these arguments and frames are often spread across the various branches of the discussion tree structure.This makes it difficult for interested participants to follow the discussion in its entirety as well as to introduce new arguments.In this paper, we present a new rankbased approach to extractive summarization of online discussions focusing on argumentation frames that capture the different aspects of a discussion.Our approach includes three retrieval tasks to find arguments in a discussion that are (1) relevant to a frame of interest, (2) relevant to the topic under discussion, and (3) informative to the reader.Based on a joint ranking by these three criteria for a set of user-selected frames, our approach allows readers to quickly access an ongoing discussion.We evaluate our approach using a test set of 100 controversial Reddit ChangeMyView discussions, for which the relevance of a total of 1871 arguments was manually annotated.

An audience's prior beliefs and morals are strong indicators of how likely they will be affected by a given argument.Utilizing such knowledge can help focus on shared values to bring disagreeing parties towards agreement.In argumentation technology, however, this is barely exploited so far.This paper studies the feasibility of automatically generating morally framed arguments as well as their effect on different audiences.Following the moral foundation theory, we propose a system that effectively generates arguments focusing on different morals.In an in-depth user study, we ask liberals and conservatives to evaluate the impact of these arguments.Our results suggest that, particularly when prior beliefs are challenged, an audience becomes more affected by morally framed arguments.

When engaging in argumentative discourse, skilled human debaters tailor claims to the audience's beliefs to construct effective arguments.Recently, the field of computational argumentation witnessed extensive effort to address the automatic generation of arguments.However, existing approaches do not perform any audience-specific adaptation.In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs.To tackle this task, we model the people's prior beliefs through their stances on controversial topics and extend state-of-the-art text generation models to generate claims conditioned on the beliefs.Our automatic evaluation confirms the ability of our approach to adapt claims to a set of given beliefs.In a manual study, we also evaluate the generated claims in terms of informativeness and their likelihood to be uttered by someone with a respective belief.Our results reveal the limitations of modeling users' beliefs based on their stances.Still, they demonstrate the potential of encoding beliefs into argumentative texts, laying the ground for future exploration of audience reach.