05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
Browse
23 results
Search Results
Item Open Access Cycle-consistent adversarial networks for automatic speech recognition(2024) Li, Chia-Yu; Vu, Ngoc Thang (Prof. Dr.)Item Open Access A computational stylistics of poetry : distant reading and modeling of German and English verse(2023) Haider, Thomas; Kuhn, Jonas (Prof. Dr.)This doctoral thesis is about the computational modeling of stylistic variation in poetry. As ‘a computational stylistics’ it examines the forms, social embedding, and the aesthetic potential of literary texts by means of computational and statistical methods, ranging from simple counting over information theoretic measures to neural network models, including experiments with representation learning, transfer learning, and multi-task learning. We built small corpora to manually annotate a number of phenomena that are relevant for poetry, such as meter, rhythm, rhyme, and also emotions and aesthetic judgements that are elicited in the reader. A strict annotation workflow allows us to better understand these phenomena, from how to conceptualize them and which problems arise when trying to annotate them on a larger scale. Furthermore, we built large corpora to discover patterns in a wide historical, aesthetic and linguistic range, with a focus on German and English writing, encompassing public domain texts from the late 16th century up into the early 20th century. These corpora are published with metadata and reliable automatic annotation of part-of-speech tags, syllable boundaries, meter and verse measures. This thesis contains chapters on diachronic variation, aesthetic emotions, and modeling prosody, including experiments that also investigate the interaction between them. We look at how the diction of poets in different languages changed over time, which topics and metaphors were and became popular, both as a reaction to aesthetic considerations and also the political climate of the time. We investigate which emotions are elicited in readers when they read poetry, how that relates to aesthetic judgements, how we can annotate such emotions, and then train models to learn them. Also, we present experiments on how to annotate prosodic devices on a large scale, how well we can train computational models to predict the prosody from text, and how informative those devices are for each other.Item Open Access Cross-lingual frame comparability : computational and linguistic perspectives(2023) Sikos, Jennifer; Padó, Sebastian (Prof. Dr.)Frames are descriptions of commonplace scenarios or events. Because they describe everyday scenes, such as buying or eating, it seems reasonable to assume that many frames in one language would carry over directly to other languages. However, the specifics of how that scene is realized can be highly specific to a culture; it is still an open research question as to how well (and how many) frames actually apply across languages. This thesis concerns cross-lingual frame comparability - the degree to which a frame can be transferred from one language to another. It addresses several aspects of frame comparability: what is frame comparability; how a computational system can measure cross-lingual frame comparability; and how frame comparability affects cross-lingual models of frames.Item Open Access Methods for mining political opinions from texts and large language models(2025) Ceron, Tanise; Padó, Sebastian (Prof. Dr.)In democratic societies, the diversity of opinions enables individuals to express their values and engage with differing perspectives. This thesis investigates political opinions through two lenses: texts and models, examining both ideological positions and policy issue preferences. While ideological analysis is well-established, policy issue preferences represent a more nuanced, underexplored research area. Investigating political opinions from political parties is essential for understanding voter choices, policy decision-making, and the shifts in party agendas over time. In the first part of this thesis, I focus on methods for mining political opinions from party manifestos. Automating the identification of political opinions helps process large datasets, minimize annotation time, and offer timely updates on newly released information from parties. I investigate how accurately party positions can be identified from texts with minimal annotations and the level of detail achievable in this process. We also explore the extent to which party positions can be identified on a large scale across different languages and countries.Results demonstrate that the identification of party positions can be distinguished between the tasks of political scaling and positioning which have substantial differences in terms of evaluation and application. Additionally, findings indicate that improving text representations through in-domain fine-tuning significantly benefits the performance when methods depend on text similarity. And finally, party scaling across languages achieves high performance with multilingual models. Models have become my object of study with the advent of LLMs. They introduce new concerns regarding the type of biases embedded and reproduced by them. Given the importance of shedding light on political biases in LLMs, the second part of this thesis addresses the evaluation and identification of political biases in LLMs. Our research questions center on robustly evaluating LLMs for biases and identifying the political biases regarding ideology and policy issue preferences. This thesis provides definitions of political bias and political worldview, which aid in designing methods for their evaluation. Moreover, it contributes with a framework for a robust evaluation of biases in LLMs and a dataset for evaluating political opinions in LLMs. Finally, findings indicate that small parameter size models are not reliable in their answers, and that LLMs do hold consistent political worldviews in relation to some policy issues. Overall, they highlight the necessity for continued research to understand the complexities and societal implications of developing models integrating diverse political opinions into AI systems.Item Open Access Understanding and adapting to user mental models in task-oriented dialog(2026) Vanderlyn, Lindsey; Vu, Ngoc Thang (Prof. Dr.)Item Open Access What makes discussions constructive? : modeling argument and deliberative quality(2025) Falk, Neele; Lapesa, Gabriella (Jun.-Prof. Dr.)Public discourse is an important building block of democracy: it is where opinions are exchanged and certain narratives are formed or reinforced. In recent years, the importance of everyday discussions, for example on social media, has grown. These platforms allow citizens from different backgrounds and with different political views to participate. The aim of the following thesis is to empirically model constructive discussion behavior. Only constructive discourse can strengthen democracy through participation and the exchange of different perspectives. If this fails, the result is fake news and declining trust in democracy—and thus a threat to any democratic system. This work takes an interdisciplinary perspective. The research is based on actual discussions between lay citizens (e.g., online discussions). The theoretical basis is provided by normative frameworks that define what constitutes a constructive discussion or a good argument (e.g., deliberative theory or the definition of good argumentation in rhetoric). These are modeled and evaluated using methods from natural language processing (NLP). NLP allows large amounts of data to be analyzed efficiently—for example, as shown in a publication of this work, all discussion posts on Reddit about the COVID-19 pandemic. The resulting models can in turn be used to develop semi-automatic interventions for more constructive discussion behavior: the moderation of (online) discussions. The thesis is divided into three parts: the first part examines how central aspects of constructive discussion behavior can be defined and how they can be automatically measured or evaluated in natural language. In the social sciences, these aspects are summarized and characterized under the core concept of "deliberative quality", while in rhetoric and computational linguistics they are treated under the concept of "argument quality". The second part of this thesis takes a closer look at a particular aspect of deliberative quality: the narration of personal experiences and stories (known as "storytelling"), a phenomenon that is often used to clarify a point of view. In the context of constructive discourse, storytelling can, for example, promote perspective-taking, is less conflict-laden than value-based argumentation, and can contribute to creating a collective identity or a shared narrative. The third part of this thesis deals with semi-automatic support for moderation. This is based on data containing human moderation behavior (e.g., from experts in the field of moderation or from active discussion participants). In this part, the findings from the first two building blocks are brought together to examine the relationship between moderation and aspects of (non-)constructive discussion behavior and, based on this, how these insights can be used to develop models for supporting human moderators.Item Open Access Challenges of computational social science analysis with NLP methods(2022) Dayanik, Erenay; Padó, Sebastian (Prof. Dr.)Computational Social Science (CSS) is an emerging research area at the intersection of social science and computer science, where problems of societal relevance can be addressed by novel computational methods. With the recent advances in machine learning and natural language processing as well as the availability of textual data, CSS has opened up to new possibilities, but also methodological challenges. In this thesis, we present a line of work on developing methods and addressing challenges in terms of data annotation and modeling for computational political science and social media analysis, two highly popular and active research areas within CSS. In the first part of the thesis, we focus on a use case from computational political science, namely Discourse Network Analysis (DNA), a framework that aims at analyzing the structures behind complex societal discussions. We investigate how this style of analysis, which is traditionally performed manually, can be automated. We start by providing a requirement analysis outlining a roadmap to decompose the complex DNA task into several conceptually simpler sub-tasks. Then, we introduce NLP models with various configurations to automate two of the sub-tasks given by the requirement analysis, namely claim detection and classification, based on different neural network architectures ranging from unidirectional LSTMs to Transformer based architectures. In the second part of the thesis, we shift our focus to fairness, a central concern in CSS. Our goal in this part of the thesis is to analyze and improve the performances of NLP models used in CSS in terms of fairness and robustness while maintaining their overall performance. With that in mind, we first analyze the above-mentioned claim detection and classification models and propose techniques to improve model fairness and overall performance. After that, we broaden our focus to social media analysis, another highly active subdomain of CSS. Here, we study text classification of the correlated attributes, which pose an important but often overlooked challenge to model fairness. Our last contribution is to discuss the limitations of the current statistical methods applied for bias identification; to propose a multivariate regression based approach; and to show that, through experiments conducted on social media data, it can be used as a complementary method for bias identification and analysis tasks. Overall, our work takes a step towards increasing the understanding of challenges of computational social science. We hope that both political scientists and NLP scholars can make use of the insights from this thesis in their research.Item Open Access Linguistically-informed modeling of potentials for misunderstanding(2024) Anthonio, Talita; Roth, Michael (Dr.)Misunderstandings are prevalent in communication. While there is a large amount of work on misunderstandings in conversations, only little attention has been given to misunderstandings that arise from text. This is because readers and writers typically do not interact with one another. However, texts that potentially evoke different interpretations can be identified by certain linguistic phenomena, especially those related to implicitness or underspecificity. In Computational Linguistics, there is a considerable amount of work conducted on such linguistic phenomena and the computational modeling thereof. However, most of these studies do not examine when these phenomena cause misunderstandings. This is a crucial aspect, because ambiguous language does not always cause misunderstanding. In this thesis, we provide the first steps to develop a computational model that can automatically identify whether an instructional text is likely to cause misunderstandings ("potentials for misunderstanding"). To achieve this goal, we build large corpora with potentials for misunderstanding in instructional texts. We follow previous work and define misunderstandings as the existence of multiple, plausible interpretations. As these interpretations may be similar in meaning to one another, we specifically define misunderstandings as the existence of multiple plausible, but conflicting interpretations. Therefore, we find texts that potentially cause misunderstanding ("potentials for misunderstanding") by looking for passages that have several plausible interpretations that are conflicting to one another. We automatically identify such passages from revision histories of instructional texts, based on the finding that we can find potentials for misunderstanding by looking into older versions of a text, and their clarifications thereof in newer versions. We specifically look for unclarified sentences that contain implicit and underspecified language, and study their clarifications. Through several analyses and crowdsourcing studies, we demonstrate that our corpora provide valuable resources on potentials for misunderstanding, as we find that revised sentences are better than their previous ones. Furthermore, we show that the provided corpora can be used for several computational modeling purposes. The three resulting models can each be combined to identify whether a text potentially causes misunderstanding or not. More specifically, we first develop a model that can detect improvements in a text, even when they are subtle and closely dependent on the context. In an analysis, we verify that the judgements from the model on what makes a better or equally good sentence overlap with the judgements by humans. Secondly, we build a transformer-based language model that automatically resolves potentials for misunderstanding caused by implicit references. We find that modeling discourse context improves the performance of this model. In an analysis, we find that the best model is not only capable of generating the golden resolution, but also capable of generating several plausible resolutions for implicit references in instructional text. We use this finding to build a large dataset with plausible and implausible resolutions of implicit and underspecified elements. We use the resulting dataset for a third computational task, in which we train a model to automatically distinguish between plausible and implausible resolutions for implicit and underspecified elements. We show that this model and the provided dataset can be used to find passages with several, plausible clarifications. Since our definition of misunderstanding focuses on conflicting clarifications, we conduct a final study to conclude the thesis. In particular, we provide and validate a crowdsourcing set-up that allows to find the cases with conflicting, plausible, resolutions. The set-up and findings could be used in future research to directly train a model to identify passages with implicit elements that have conflicting resolutions.Item Open Access Multilingual and controllable text-to-speech synthesis for low-resource scenarios(2025) Lux, Florian; Vu, Ngoc Thang (Prof. Dr.)Dissertation von Florian Lux zum Thema der mehrsprachingen Sprachsynthese in Szenarien, bei denen die Menge an Trainingsdaten konventionelle Methoden des maschinellen Lernens nicht zulassen.Item Open Access Syntactic dependencies and beyond : robust neural architectures and quality-enhanced corpora for structured prediction in NLP(2025) Grünewald, Stefan; Kuhn, Jonas (Prof. Dr.)This thesis investigates explicit structure in Natural Language Processing (NLP). Such structure, represented by abstract linguistic objects like part-of-speech tags, syntax trees, or graph-based meaning representations, has traditionally played a central role in NLP. Historically linked to the idea of rule-based processing of human language (using tools such as formal grammars), it has also successfully been combined with statistical machine learning techniques. For practical applications, this has often taken the form of a pipeline in which the prediction of linguistic features serves as a first step towards addressing “higher-level” tasks such as text classification or information extraction. In addition, algorithmic extraction of linguistic structures is also being pursued for its own sake, i.e., as a means to deepen our understanding of human language. Most recently, the field of NLP has been dominated by techniques leveraging artificial neural networks. In this paradigm, language data is not processed using a pipeline approach as outlined above, which is ultimately grounded in simple and interpretable features. Rather, neural networks learn internal, vector-based language representations by means of large-scale mathematical optimization based on (usually very large amounts of) raw input data, allowing for “end-to-end” language processing that does not involve any kind of explicit structure as an intermediate representation. While the successes of this paradigm are undeniable in practical terms - i.e., achieving new state-of-the-art results on a wide range of applications ranging from information extraction to machine translation -, it has also spurred controversial questions around the present and future role of explicit structure in NLP. At an overarching level, this means a general uncertainty about the role of explicit structure: Can NLP still benefit from modeling structure explicitly, or have such approaches become obsolete? Apart from this fundamental question, however, the interaction between neural networks and explicit structure in NLP also raises a number of practical challenges; and it is these challenges that form the core of this thesis and the basis for its contributions. The first challenge relates to the role of data in training structure-prediction systems. As a general rule, neural networks require large amounts of (labeled) training data for learning specific tasks, and thus the curation and annotation of suitable datasets is a common bottleneck in their development. In our contributions, we focus on the Universal Dependencies (UD) formalism for the annotation of syntactic dependencies. Evaluating the quality of existing treebanks and examining ways of improving and extending them, one of our core findings it that both rule-based and machine learning-based methods can be leveraged to reduce the need for manual annotation. The second challenge relates to the design and architecture of neural structure-predicting systems, of which there exists a wide variety; often, it is not fully clear which factors are truly important in achieving the best possible performance. We study dependency parser architectures for UD parsing, finding that when using modern neural network backbones, simpler is often better, with more sophisticated setups offering little in the way of performance improvements. The third challenge relates to structure prediction for downstream NLP tasks. Here, we investigate the tasks of Negation Resolution and Relation Extraction by means of framing them as graph parsing problems and utilizing neural architectures similar to those studied for dependency parsing. We find that such an approach generally yields robust results, but is not clearly superior to “shallow” sequence labeling. In sum, we hope that our contributions serve to inform and inspire future research on the role of explicit structure in NLP, and more generally within the emerging paradigm of artificial intelligence (AI) that combines neural networks with rule-based algorithms and symbolic representations (“neuro-symbolic AI”).
- «
- 1 (current)
- 2
- 3
- »