Heiner Stuckenschmidt


Come hither or go away? Recognising pre-electoral coalition signals in the news
Ines Rehbein | Simone Paolo Ponzetto | Anna Adendorf | Oke Bahnsen | Lukas Stoetzer | Heiner Stuckenschmidt
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

In this paper, we introduce the task of political coalition signal prediction from text, that is, the task of recognizing from the news coverage leading up to an election the (un)willingness of political parties to form a government coalition. We decompose our problem into two related, but distinct tasks: (i) predicting whether a reported statement from a politician or a journalist refers to a potential coalition and (ii) predicting the polarity of the signal – namely, whether the speaker is in favour of or against the coalition. For this, we explore the benefits of multi-task learning and investigate which setup and task formulation is best suited for each sub-task. We evaluate our approach, based on hand-coded newspaper articles, covering elections in three countries (Ireland, Germany, Austria) and two languages (English, German). Our results show that the multi-task learning approach can further improve results over a strong monolingual transfer learning baseline.


Exploring Morality in Argumentation
Jonathan Kobbe | Ines Rehbein | Ioana Hulpuș | Heiner Stuckenschmidt
Proceedings of the 7th Workshop on Argument Mining

Sentiment and stance are two important concepts for the analysis of arguments. We propose to add another perspective to the analysis, namely moral sentiment. We argue that moral values are crucial for ideological debates and can thus add useful information for argument mining. In the paper, we present different models for automatically predicting moral sentiment in debates and evaluate them on a manually annotated testset. We then apply our models to investigate how moral values in arguments relate to argument quality, stance and audience reactions.

Unsupervised stance detection for arguments from consequences
Jonathan Kobbe | Ioana Hulpuș | Heiner Stuckenschmidt
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Social media platforms have become an essential venue for online deliberation where users discuss arguments, debate, and form opinions. In this paper, we propose an unsupervised method to detect the stance of argumentative claims with respect to a topic. Most related work focuses on topic-specific supervised models that need to be trained for every emergent debate topic. To address this limitation, we propose a topic independent approach that focuses on a frequently encountered class of arguments, specifically, on arguments from consequences. We do this by extracting the effects that claims refer to, and proposing a means for inferring if the effect is a good or bad consequence. Our experiments provide promising results that are comparable to, and in particular regards even outperform BERT. Furthermore, we publish a novel dataset of arguments relating to consequences, annotated with Amazon Mechanical Turk.

Predicting Modality in Financial Dialogue
Kilian Theil | Heiner Stuckenschmidt
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation

In this paper, we perform modality prediction in financial dialogue. To this end, we introduce a new dataset and develop a binary classifier to detect strong or weak modal answers depending on surface, lexical, and semantic representations of the preceding question and financial features. To do so, we contrast different algorithms, feature categories, and fusion methods. Perhaps counter-intuitively, our results indicate that the strongest features for the given task are financial uncertainty measures such as market and individual firm risk.

Knowledge Graphs meet Moral Values
Ioana Hulpuș | Jonathan Kobbe | Heiner Stuckenschmidt | Graeme Hirst
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics

Operationalizing morality is crucial for understanding multiple aspects of society that have moral values at their core – such as riots, mobilizing movements, public debates, etc. Moral Foundations Theory (MFT) has become one of the most adopted theories of morality partly due to its accompanying lexicon, the Moral Foundation Dictionary (MFD), which offers a base for computationally dealing with morality. In this work, we exploit the MFD in a novel direction by investigating how well moral values are captured by KGs. We explore three widely used KGs, and provide concept-level analogues for the MFD. Furthermore, we propose several Personalized PageRank variations in order to score all the concepts and entities in the KGs with respect to their relevance to the different moral values. Our promising results help to progress the operationalization of morality in both NLP and KG communities.


A Spreading Activation Framework for Tracking Conceptual Complexity of Texts
Ioana Hulpuș | Sanja Štajner | Heiner Stuckenschmidt
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We propose an unsupervised approach for assessing conceptual complexity of texts, based on spreading activation. Using DBpedia knowledge graph as a proxy to long-term memory, mentioned concepts become activated and trigger further activation as the text is sequentially traversed. Drawing inspiration from psycholinguistic theories of reading comprehension, we model memory processes such as semantic priming, sentence wrap-up, and forgetting. We show that our models capture various aspects of conceptual text complexity and significantly outperform current state of the art.


Word Embeddings-Based Uncertainty Detection in Financial Disclosures
Christoph Kilian Theil | Sanja Štajner | Heiner Stuckenschmidt
Proceedings of the First Workshop on Economics and Natural Language Processing

In this paper, we use NLP techniques to detect linguistic uncertainty in financial disclosures. Leveraging general-domain and domain-specific word embedding models, we automatically expand an existing dictionary of uncertainty triggers. We furthermore examine how an expert filtering affects the quality of such an expansion. We show that the dictionary expansions significantly improve regressions on stock return volatility. Lastly, we prove that the expansions significantly boost the automatic detection of uncertain sentences.


Sentence Alignment Methods for Improving Text Simplification Systems
Sanja Štajner | Marc Franco-Salvador | Simone Paolo Ponzetto | Paolo Rosso | Heiner Stuckenschmidt
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We provide several methods for sentence-alignment of texts with different complexity levels. Using the best of them, we sentence-align the Newsela corpora, thus providing large training materials for automatic text simplification (ATS) systems. We show that using this dataset, even the standard phrase-based statistical machine translation models for ATS can outperform the state-of-the-art ATS systems.


Lost in Discussion? Tracking Opinion Groups in Complex Political Discussions by the Example of the FOMC Meeting Transcriptions
Cäcilia Zirn | Robert Meusel | Heiner Stuckenschmidt
Proceedings of the International Conference Recent Advances in Natural Language Processing


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Towards Distributed MCMC Inference in Probabilistic Knowledge Bases
Mathias Niepert | Christian Meilicke | Heiner Stuckenschmidt
Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX)


Fine-Grained Sentiment Analysis with Structural Features
Cäcilia Zirn | Mathias Niepert | Heiner Stuckenschmidt | Michael Strube
Proceedings of 5th International Joint Conference on Natural Language Processing