Recent state-of-the-art authorship attribution methods learn authorship representations of text in a latent, uninterpretable space, which hinders their usability in real-world applications. We propose a novel approach for interpreting learned embeddings by identifying representative points in the latent space and leveraging large language models to generate informative natural language descriptions of the writing style associated with each point. We evaluate the alignment between our interpretable and latent spaces and demonstrate superior prediction agreement over baseline methods. Additionally, we conduct a human evaluation to assess the quality of these style descriptions and validate their utility in explaining the latent space. Finally, we show that human performance on the challenging authorship attribution task improves by +20% on average when aided with explanations from our method.

We propose a new approach for the authorship attribution task that leverages the various linguistic representations learned at different layers of pre-trained transformer-based models.We evaluate our approach on two popular authorship attribution models and three evaluation datasets, in in-domain and out-of-domain scenarios.We found that utilizing various transformer layers improves the robustness of authorship attribution models when tested on outof-domain data, resulting in a much stronger performance.Our analysis gives further insights into how our model's different layers get specialized in representing certain linguistic aspects that we believe benefit the model when tested out of the domain.

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.

Explanations are pervasive in our lives. Mostly, they occur in dialogical form where an explainer discusses a concept or phenomenon of interest with an explainee. Leaving the explainee with a clear understanding is not straightforward due to the knowledge gap between the two participants. Previous research looked at the interaction of explanation moves, dialogue acts, and topics in successful dialogues with expert explainers. However, daily-life explanations often fail, raising the question of what makes a dialogue successful. In this work, we study explanation dialogues in terms of the interactions between the explainer and explainee and how they correlate with the quality of explanations in terms of a successful understanding on the explainee’s side. In particular, we first construct a corpus of 399 dialogues from the Reddit forum Explain Like I am Five and annotate it for interaction flows and explanation quality. We then analyze the interaction flows, comparing them to those appearing in expert dialogues. Finally, we encode the interaction flows using two language models that can handle long inputs, and we provide empirical evidence for the effectiveness boost gained through the encoding in predicting the success of explanation dialogues.

While human values play a crucial role in making arguments persuasive, we currently lack the necessary extensive datasets to develop methods for analyzing the values underlying these arguments on a large scale. To address this gap, we present the Touché23-ValueEval dataset, an expansion of the Webis-ArgValues-22 dataset. We collected and annotated an additional 4780 new arguments, doubling the dataset’s size to 9324 arguments. These arguments were sourced from six diverse sources, covering religious texts, community discussions, free-text arguments, newspaper editorials, and political debates. Each argument is annotated by three crowdworkers for 54 human values, following the methodology established in the original dataset. The Touché23-ValueEval dataset was utilized in the SemEval 2023 Task 4. ValueEval: Identification of Human Values behind Arguments, where an ensemble of transformer models demonstrated state-of-the-art performance. Furthermore, our experiments show that a fine-tuned large language model, Llama-2-7B, achieves comparable results.

Meghdut Sengupta, Roxanne El Baff, Milad Alshomary, Henning Wachsmuth. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

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 enforce 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 stanceadhering counters than strong baselines.

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.

Johannes Kiesel, Milad Alshomary, Nicolas Handke, Xiaoni Cai, Henning Wachsmuth, Benno Stein. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

As AI is more and more pervasive in everyday life, humans have an increasing demand to understand its behavior and decisions. Most research on explainable AI builds on the premise that there is one ideal explanation to be found. In fact, however, everyday explanations are co-constructed in a dialogue between the person explaining (the explainer) and the specific person being explained to (the explainee). In this paper, we introduce a first corpus of dialogical explanations to enable NLP research on how humans explain as well as on how AI can learn to imitate this process. The corpus consists of 65 transcribed English dialogues from the Wired video series 5 Levels, explaining 13 topics to five explainees of different proficiency. All 1550 dialogue turns have been manually labeled by five independent professionals for the topic discussed as well as for the dialogue act and the explanation move performed. We analyze linguistic patterns of explainers and explainees, and we explore differences across proficiency levels. BERT-based baseline results indicate that sequence information helps predicting topics, acts, and moves effectively.

In argument search, snippets provide an overview of the aspects discussed by the arguments retrieved for a queried controversial topic. Existing work has focused on generating snippets that are representative of an argument’s content while remaining argumentative. In this work, we argue that the snippets should also be contrastive, that is, they should highlight the aspects that make an argument unique in the context of others. Thereby, aspect diversity is increased and redundancy is reduced. We present and compare two snippet generation approaches that jointly optimize representativeness and contrastiveness. According to our experiments, both approaches have advantages, and one is able to generate representative yet sufficiently contrastive snippets.

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.

In argumentation, people state premises to reason towards a conclusion.The conclusion conveys a stance towards some target, such as a concept or statement.Often, the conclusion remains implicit, though, since it is self-evident in a discussion or left out for rhetorical reasons.However, the conclusion is key to understanding an argument, and hence, to any application that processes argumentation.We thus study the question to what extent an argument's conclusion can be reconstructed from its premises.In particular, we argue here that a decisive step is to infer a conclusion's target, and we hypothesize that this target is related to the premises' targets.We develop two complementary target inference approaches: one ranks premise targets and selects the top-ranked target as the conclusion target, the other finds a new conclusion target in a learned embedding space using a triplet neural network.Our evaluation on corpora from two domains indicates that a hybrid of both approaches is best, outperforming several strong baselines.According to human annotators, we infer a reasonably adequate conclusion target in 89% of the cases.

Snippets are used in web search to help users assess the relevance of retrieved results to their query. Recently, specialized search engines have arisen that retrieve pro and con arguments on controversial issues. We argue that standard snippet generation is insufficient to represent the core reasoning of an argument. In this paper, we introduce the task of generating a snippet that represents the main claim and reason of an argument. We propose a query-independent extractive summarization approach to this task that uses a variant of PageRank to assess the importance of sentences based on their context and argumentativeness. In both automatic and manual evaluation, our approach outperforms strong baselines.

Yamen Ajjour, Milad Alshomary, Henning Wachsmuth, Benno Stein. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Human is surrounded by a tremendous amount of information on the web. That highlights the continuous need of recommendation systems in the different domains. Unfortunately cold start problem is still an important issue in these systems on new users and new items. The problem becomes more critical in systems that contain resources that lives too shortly like offers on products which stays only for few days (short life resources - SLiR), or news in a news site. From the other side social networks are very rich with users' information, unfortunately most of the proposed social recommender are applied on domain specific social networks like flickers and epinions which are much less used in the day to day life, because dealing with General Purpose Social Network (GPSN) like Facebook and Twitter needs to transform these GPSN into a useful source of recommendation dealing with them as row, implicit or unary data. In this work we highlight how iSoNTRE (the intelligent Social Network Transformer into Recommendation Engine) addresses this challenge by transforming the GPSN into useful information for recommendation based on middle layer of domain concepts. iSoNTRE overcomes the cold start problem on new users and items. It has been evaluated over Twitter, on new users, recommending offers as a kind of SLiR, results showed that iSoNTRE succeeded in recommending good offers with 14% of click on recommended offers, which is high compared to general open rate in social media, especially when we have nothing about users and we are recommending SLiR resources.