In this paper, we introduce the task of style-consistent content transfer, which concerns modifying a text’s content based on a provided reference statement while preserving its original style. We approach the task by employing multi-task learning to ensure that the modified text meets three important conditions: reference faithfulness, style adherence, and coherence. In particular, we train three independent classifiers for each condition. During inference, these classifiers are used to determine the best modified text variant. Our evaluation, conducted on hotel reviews and news articles, compares our approach with sequence-to-sequence and error correction baselines. The results demonstrate that our approach reasonably generates text satisfying all three conditions. In subsequent analyses, we highlight the strengths and limitations of our approach, providing valuable insights for future research directions.

We propose misogyny detection as an Argumentative Reasoning task and we investigate the capacity of large language models (LLMs) to understand the implicit reasoning used to convey misogyny in both Italian and English.The central aim is to generate the missing reasoning link between a message and the implied meanings encoding the misogyny.Our study uses argumentation theory as a foundation to form a collection of prompts in both zero-shot and few-shot settings.These prompts integrate different techniques, including chainof-thought reasoning and augmented knowledge.Our findings show that LLMs fall short on reasoning capabilities about misogynistic comments relying on their implicit knowledge derived from internalized common stereotypes about women to generate implied assumptions, rather than on inductive reasoning.

Online forums encourage the exchange and discussion of different stances on many topics.Not only do they provide an opportunity to present one's own arguments, but may also gather a broad cross-section of others' arguments.However, the resulting long discussions are difficult to overview.This paper presents a novel unsupervised approach using large language models (LLMs) to generating indicative summaries for long discussions that basically serve as tables of contents.Our approach first clusters argument sentences, generates cluster labels as abstractive summaries, and classifies the generated cluster labels into argumentation frames resulting in a two-level summary.Based on an extensively optimized prompt engineering approach, we evaluate 19 LLMs for generative cluster labeling and frame classification.To evaluate the usefulness of our indicative summaries, we conduct a purpose-driven user study via a new visual interface called DISCUS-SION EXPLORER: It shows that our proposed indicative summaries serve as a convenient navigation tool to explore long discussions.

Existing datasets for causality identification in argumentative texts have several limitations, such as the type of input text (e.g., only claims), causality type (e.g., only positive), and the linguistic patterns investigated (e.g., only verb connectives).To resolve these limitations, we build the WEBIS-CAUSALITY-23 dataset, with sophisticated inputs (all units from arguments), a balanced distribution of causality types, and a larger number of linguistic patterns denoting causality.The dataset contains 1485 examples derived by combining the two paradigms of distant supervision and uncertainty sampling to identify diverse, high-quality samples of causality relations, and annotate them in a costeffective manner.

Existing studies on the analysis of persuasion in online discussions focus on investigating the effectiveness of comments in discussions and ignore the analysis of the effectiveness of debaters over multiple discussions. In this paper, we propose to quantify debaters effectiveness in the online discussion platform: “ChangeMyView” in order to explore diverse insights into their persuasion strategies. In particular, targeting debaters with different levels of effectiveness (e.g., good vs. bad), various behavioral characteristics (e..g, engagement) and text stylistic features (e.g., used frames) of debaters are carefully examined, leading to several outcomes that can be the backbone of writing assistants and persuasive text generation.

Khalid Al Khatib, Lukas Trautner, Henning Wachsmuth, Yufang Hou, Benno Stein. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

This paper studies the end-to-end construction of an argumentation knowledge graph that is intended to support argument synthesis, argumentative question answering, or fake news detection, among others. The study is motivated by the proven effectiveness of knowledge graphs for interpretable and controllable text generation and exploratory search. Original in our work is that we propose a model of the knowledge encapsulated in arguments. Based on this model, we build a new corpus that comprises about 16k manual annotations of 4740 claims with instances of the model's elements, and we develop an end-to-end framework that automatically identifies all modeled types of instances. The results of experiments show the potential of the framework for building a web-based argumentation graph that is of high quality and large scale.

News editorials argue about political issues in order to challenge or reinforce the stance of readers with different ideologies.Previous research has investigated such persuasive effects for argumentative content.In contrast, this paper studies how important the style of news editorials is to achieve persuasion.To this end, we first compare content-and style-oriented classifiers on editorials from the liberal NYTimes with ideology-specific effect annotations.We find that conservative readers are resistant to NYTimes style, but on liberals, style even has more impact than content.Focusing on liberals, we then cluster the leads, bodies, and endings of editorials, in order to learn about writing style patterns of effective argumentation.

This paper introduces the Webis Gmane Email Corpus 2019, the largest publicly available and fully preprocessed email corpus to date.We crawled more than 153 million emails from 14,699 mailing lists and segmented them into semantically consistent components using a new neural segmentation model.With 96% accuracy on 15 classes of email segments, our model achieves state-of-the-art performance while being more efficient to train than previous ones.All data, code, and trained models are made freely available alongside the paper. 1

Predicting the persuasiveness of arguments has applications as diverse as writing assistance, essay scoring, and advertising. While clearly relevant to the task, the personal characteristics of an argument's source and audience have not yet been fully exploited toward automated persuasiveness prediction. In this paper, we model debaters' prior beliefs, interests, and personality traits based on their previous activity, without dependence on explicit user profiles or questionnaires. Using a dataset of over 60,000 argumentative discussions, comprising more than three million individual posts collected from the subreddit r/ChangeMyView, we demonstrate that our modeling of debater's characteristics enhances the prediction of argument persuasiveness as well as of debaters' resistance to persuasion.

The automatic summarization of argumentative texts has hardly been explored.This paper takes a further step in this direction, targeting news editorials, i.e., opinionated articles with a well-defined argumentation structure.With Webis-EditorialSum-2020, we present a corpus of 1330 carefully curated summaries for 266 news editorials.We evaluate these summaries based on a tailored annotation scheme, where a high-quality summary is expected to be thesis-indicative, persuasive, reasonable, concise, and self-contained.Our corpus contains at least three high-quality summaries for about 90% of the editorials, rendering it a valuable resource for the development and evaluation of summarization technology for long argumentative texts.We further report details of both, an in-depth corpus analysis, and the evaluation of two extractive summarization models.

Synthesis approaches in computational argumentation so far are restricted to generating claim-like argument units or short summaries of debates.Ultimately, however, we expect computers to generate whole new arguments for a given stance towards some topic, backing up claims following argumentative and rhetorical considerations.In this paper, we approach such an argumentation synthesis as a language modeling task.In our language model, argumentative discourse units are the "words", and arguments represent the "sentences".Given a pool of units for any unseen topic-stance pair, the model selects a set of unit types according to a basic rhetorical strategy (logos vs. pathos), arranges the structure of the types based on the units' argumentative roles, and finally "phrases" an argument by instantiating the structure with semantically coherent units from the pool.Our evaluation suggests that the model can, to some extent, mimic the human synthesis of strategy-specific arguments.

Khalid Al-Khatib, Henning Wachsmuth, Kevin Lang, Jakob Herpel, Matthias Hagen, Benno Stein. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.

Persuasion is rarely achieved through a loose set of arguments alone. Rather, an effective delivery of arguments follows a rhetorical strategy, combining logical reasoning with appeals to ethics and emotion. We argue that such a strategy means to select, arrange, and phrase a set of argumentative discourse units. In this paper, we model rhetorical strategies for the computational synthesis of effective argumentation. In a study, we let 26 experts synthesize argumentative texts with different strategies for 10 topics. We find that the experts agree in the selection significantly more when following the same strategy. While the texts notably vary for different strategies, especially their arrangement remains stable. The results suggest that our model enables a strategical synthesis.

News editorials are said to shape public opinion, which makes them a powerful tool and an important source of political argumentation.However, rarely do editorials change anyone's stance on an issue completely, nor do they tend to argue explicitly (but rather follow a subtle rhetorical strategy).So, what does argumentation quality mean for editorials then?We develop the notion that an effective editorial challenges readers with opposing stance, and at the same time empowers the arguing skills of readers that share the editorial's stance -or even challenges both sides.To study argumentation quality based on this notion, we introduce a new corpus with 1000 editorials from the New York Times, annotated for their perceived effect along with the annotators' political orientations.Analyzing the corpus, we find that annotators with different orientation disagree on the effect significantly.While only 1% of all editorials changed anyone's stance, more than 5% meet our notion.We conclude that our corpus serves as a suitable resource for studying the argumentation quality of news editorials.

This paper introduces the task of "flipping" the bias of news articles: Given an article with a political bias (left or right), generate an article with the same topic but opposite bias.To study this task, we create a corpus with bias-labeled articles from allsides.com.As a first step, we analyze the corpus and discuss intrinsic characteristics of bias.They point to the main challenges of bias flipping, which in turn lead to a specific setting in the generation process.The paper in hand narrows down the general bias flipping task to focus on bias flipping for news article headlines.A manual annotation of headlines from each side reveals that they are self-informative in general and often convey bias.We apply an autoencoder incorporating information from an article's content to learn how to automatically flip the bias.From 200 generated headlines, 73 are classified as understandable by annotators, and 83 maintain the topic while having opposite bias.Insights from our analysis shed light on how to solve the main challenges of bias flipping.

This paper presents an analysis of argumentation strategies in news editorials within and across topics.Given nearly 29,000 argumentative editorials from the New York Times, we develop two machine learning models, one for determining an editorial's topic, and one for identifying evidence types in the editorial.Based on the distribution and structure of the identified types, we analyze the usage patterns of argumentation strategies among 12 different topics.We detect several common patterns that provide insights into the manifestation of argumentation strategies.Also, our experiments reveal clear correlations between the topics and the detected patterns.

Many argumentative texts, and news editorials in particular, follow a specific strategy to persuade their readers of some opinion or attitude. This includes decisions such as when to tell an anecdote or where to support an assumption with statistics, which is reflected by the composition of different types of argumentative discourse units in a text. While several argument mining corpora have recently been published, they do not allow the study of argumentation strategies due to incomplete or coarse-grained unit annotations. This paper presents a novel corpus with 300 editorials from three diverse news portals that provides the basis for mining argumentation strategies. Each unit in all editorials has been assigned one of six types by three annotators with a high Fleiss’ Kappa agreement of 0.56. We investigate various challenges of the annotation process and we conduct a first corpus analysis. Our results reveal different strategies across the news portals, exemplifying the benefit of studying editorials—a so far underresourced text genre in argument mining.

Argument mining aims to determine the argumentative structure of texts. Although it is said to be crucial for future applications such as writing support systems, the benefit of its output has rarely been evaluated. This paper puts the analysis of the output into the focus. In particular, we investigate to what extent the mined structure can be leveraged to assess the argumentation quality of persuasive essays. We find insightful statistical patterns in the structure of essays. From these, we derive novel features that we evaluate in four argumentation-related essay scoring tasks. Our results reveal the benefit of argument mining for assessing argumentation quality. Among others, we improve the state of the art in scoring an essay’s organization and its argument strength.

Khalid Al-Khatib, Henning Wachsmuth, Matthias Hagen, Jonas Köhler, Benno Stein. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.