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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 18
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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 -
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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PublicationSupporting Cognitive and Emotional Empathic Writing of StudentsWe present an annotation approach to capturing emotional and cognitive empathy in student-written peer reviews on business models in German. We propose an annotation scheme that allows us to model emotional and cognitive empathy scores based on three types of review components. Also, we conducted an annotation study with three annotators based on 92 student essays to evaluate our annotation scheme. The obtained inter-rater agreement of α = 0.79 for the components and the π = 0.41 for the empathy scores indicate that the proposed annotation scheme successfully guides annotators to a substantial to moderate agreement. Moreover, we trained predictive models to detect the annotated empathy structures and embedded them in an adaptive writing support system for students to receive individual empathy feedback independent of an instructor, time, and location. We evaluated our tool in a peer learning exercise with 58 students and found promising results for perceived empathy skill learning, perceived feedback accuracy, and intention to use. Finally, we present our freely available corpus of 500 empathy-annotated, student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of empathy support systems.Type: journal articleJournal: The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)
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PublicationWhen Truth Matters - Addressing Pragmatic Categories in Natural Language Inference (NLI) by Large Language Models (LLMs)( 2023-07)
;Kalouli, Aikaterini-LidaIn this paper, we focus on the ability of large language models (LLMs) to accommodate different pragmatic sentence types, such as questions, commands, as well as sentence fragments for natural language inference (NLI). On the commonly used notion of logical inference, nothing can be inferred from a question, a command, or an incomprehensible sentence fragment. We find MNLI, arguably the most important NLI dataset, and hence models fine-tuned on this dataset, insensitive to this fact. Using a symbolic semantic parser, we develop and make publicly available, fine-tuning datasets designed specifically to address this issue, with promising results. We also make a first exploration of ChatGPT's concept of entailment.Type: conference paperJournal: Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023) -
PublicationEnhancing Educational Dialogues: A Reinforcement Learning Approach for Generating AI Teacher Responses(Association for Computational Linguistics, 2023-07-13)Reinforcement Learning remains an underutilized method of training and fine-tuning Language Models (LMs) despite recent successes. This paper presents a simple approach of finetuning a language model with Reinforcement Learning to achieve competitive performance on the BEA 2023 Shared Task whose goal is to automatically generate teacher responses in educational dialogues. We utilized the novel NLPO algorithm that masks out tokens during generation to direct the model towards generations that maximize a reward function. We show results for both the t5-base model with 220 million parameters from the HuggingFace repository submitted to the leaderboard that, despite its comparatively small size, has achieved a good performance on both test and dev set, as well as GPT-2 with 124 million parameters. The presented results show that despite maximizing only one of the metrics used in the evaluation as a reward function our model scores highly in the other metrics as well.Type: conference paperJournal: Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)Volume: Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
Scopus© Citations 1 -
PublicationShallow Discourse Parsing for Open Information Extraction and Text Simplification(International Conference on Computational Linguistics, 2022-10)
;Freitas, AndréType: conference paper -
PublicationAL: An Adaptive Learning Support System for Argumentation Skills(ACM CHI Conference on Human Factors in Computing Systems, 2020-04)Recent advances in Natural Language Processing (NLP) bear the opportunity to analyze the argumentation quality of texts. This can be leveraged to provide students with individual and adaptive feedback in their personal learning journey. To test if individual feedback on students' argumentation will help them to write more convincing texts, we developed AL, an adaptive IT tool that provides students with feedback on the argumentation structure of a given text. We compared AL with 54 students to a proven argumentation support tool. We found students using AL wrote more convincing texts with better formal quality of argumentation compared to the ones using the traditional approach. The measured technology acceptance provided promising results to use this tool as a feedback application in different learning settings. The results suggest that learning applications based on NLP may have a beneficial use for developing better writing and reasoning for students in traditional learning settings.Type: conference paperJournal: ACM CHI Conference on Human Factors in Computing Systems
Scopus© Citations 66 -
PublicationA Corpus for Argumentative Writing Support in GermanIn this paper, we present a novel annotation approach to capture claims and premises of arguments and their relations in student-written persuasive peer reviews on business models in German language. We propose an annotation scheme based on annotation guidelines that allows to model claims and premises as well as support and attack relations for capturing the structure of argumentative discourse in student-written peer reviews. We conduct an annotation study with three annotators on 50 persuasive essays to evaluate our annotation scheme. The obtained inter-rater agreement of α = 0.57 for argument components and α = 0.49 for argumentative relations indicates that the proposed annotation scheme successfully guides annotators to moderate agreement. Finally, we present our freely available corpus of 1,000 persuasive student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of argumentative writing support systems for students.Type: conference paperJournal: International Conference on Computational Linguistics (COLING)
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PublicationDisSim: A Discourse-Aware Syntactic Text Simplification Framework for English and German( 2019)
;Freitas, AndréWe introduce DisSim, a discourse-aware sentence splitting framework for English and German whose goal is to transform syntactically complex sentences into an intermediate representation that presents a simple and more regular structure which is easier to process for downstream semantic applications. For this purpose, we turn input sentences into a two-layered semantic hierarchy in the form of core facts and accompanying contexts, while identifying the rhetorical relations that hold between them. In that way, we preserve the coherence structure of the input and, hence, its interpretability for downstream tasks.Type: conference paper