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Siegfried Handschuh
Title
Prof. Dr.
Last Name
Handschuh
First name
Siegfried
Email
siegfried.handschuh@unisg.ch
Phone
+41 71 224 3441
Now showing
1 - 10 of 45
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PublicationCan GenAI do your next strategy task? Not yet.( 2024)
;Lang, N. ;Bouffault, O. ;Cooper, J.Type: journal articleJournal: California Management Review -
PublicationCapturing the Varieties of Natural Language Inference: A Systematic Survey of Existing Datasets and Two Novel Benchmarks( 2023-11-20)
;Katis, IoannisTransformer-based Pre-Trained Language Models currently dominate the field of Natural Language Inference (NLI). We first survey existing NLI datasets, and we systematize them according to the different kinds of logical inferences that are being distinguished. This shows two gaps in the current dataset landscape, which we propose to address with one dataset that has been developed in argumentative writing research as well as a new one building on syllogistic logic. Throughout, we also explore the promises of ChatGPT. Our results show that our new datasets do pose a challenge to existing methods and models, including ChatGPT, and that tackling this challenge via fine-tuning yields only partly satisfactory results.Type: journal articleJournal: Journal of Logic, Language and Information -
PublicationA Canonical Context-Preserving Representation for Open IE: Extracting Semantically Typed Relational Tuples from Complex Sentences(Elsevier, 2023-05-23)
;Freitas, AndréModern systems that deal with inference in texts need automatized methods to extract meaning representations (MRs) from texts at scale. Open Information Extraction (IE) is a prominent way of extracting all potential relations from a given text in a comprehensive manner. Previous work in this area has mainly focused on the extraction of isolated relational tuples. Ignoring the cohesive nature of texts where important contextual information is spread across clauses or sentences, state-of-the- art Open IE approaches are thus prone to generating a loose arrangement of tuples that lack the expressiveness needed to infer the true meaning of complex assertions. To overcome this limitation, we present a method that allows existing Open IE systems to enrich their output with additional meta information. By leveraging the semantic hierarchy of minimal propositions generated by the discourse-aware Text Simplification (TS) approach presented in Niklaus et al. (2019), we propose a mechanism to extract semantically typed relational tuples from complex source sentences. Based on this novel type of output, we introduce a lightweight semantic representation for Open IE in the form of normalized and context-preserving relational tuples. It extends the shallow semantic representation of state-of-the-art approaches in the form of predicate-argument structures by capturing intra-sentential rhetorical structures and hierarchical relationships between the relational tuples. In that way, the semantic context of the extracted tuples is preserved, resulting in more informative and coherent predicate-argument structures which are easier to interpret. In addition, in a comparative analysis, we show that the semantic hierarchy of minimal propositions benefits Open IE approaches in a second dimension: the canonical structure of the simplified sentences is easier to process and analyze, and thus facilitates the extraction of relational tuples, resulting in an improved precision (up to 32%) and recall (up to 30%) of the extracted relations on a large benchmark corpus.Type: journal articleJournal: Knowledge-Based SystemsIssue: 268 -
PublicationOrientierung und erste Empfehlungen für das Gymnasium( 2023-05-09)
;Franz EberleType: journal articleJournal: Gymnasium HelveticumVolume: 2 -
PublicationContext Matters: A Pragmatic Study of PLMs’ Negation UnderstandingIn linguistics, there are two main perspectives on negation: a semantic and a pragmatic view. So far, research in NLP on negation has almost exclusively adhered to the semantic view. In this article, we adopt the pragmatic paradigm to conduct a study of negation understanding focusing on transformer-based PLMs. Our results differ from previous, semantics-based studies and therefore help to contribute a more comprehensive – and, given the results, much more optimistic – picture of the PLMs’ negation understanding.Type: journal articleJournal: Proceedings of the 60th Annual Meeting of the Association for Computational LinguisticsVolume: 1
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PublicationA Philosophically-Informed Contribution to the Generalization Problem of Neural Natural Language Inference: Shallow Heuristics, Bias, and the Varieties of Inference(Association for Computational Linguistics, 2022)Type: journal article
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PublicationThe Textbook Learns to Talk: How to Design Chatbot-Mediated Learning to Foster Collaborative High-Order Learning?(Association for the Advancement of Computing in Education (AACE), 2021-11-09)Type: journal article
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PublicationType: journal articleIssue: arXiv:2108.11215
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PublicationFostering Students' Academic Writing Skills: Feedback Model for an AI-enabled Support Environment.(Association for the Advancement of Computing in Education (AACE), 2021-11-09)Due to recent advances in natural language processing (NLP), a new generation of digital learning support systems is emerging, which make it possible to analyse the writing quality of texts offering individual, linguistic feedback to writers through various kinds of automated text evaluation. These intelligent tutoring systems (ITS) have to be integrated into existing teaching practices alongside traditional feedback providers (e.g., tutor, peer students). Therefore, this paper explores how academic writing skills of students could be fostered by providing different types of feedback from a tutor, peer students and an ITS. It proposes a feedback model for academic writing in an AI-enabled learning support environment and illustrates the importance of the different feedback providers in an academic writing use case. Through this, the paper aims to contribute to a better understanding of the changing nature of how students' academic writing skills can be fostered in the age of artificial intelligence.Type: journal article