Rositsa Ivanova
Last Name
Ivanova
First name
Rositsa
Email
rositsa.ivanova@unisg.ch
4 results
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Item type:Publication, The Shift from Logic to Dialectic in Argumentation Theory: Implications for Computational Argument Quality Assessment(2025-01-20); In the field of computational argument quality assessment, logic and dialectic are essential dimensions used to measure the quality of argumentative texts. Both of them have found their way into the field due to their importance to argumentation theory. We trace the development of core logical concepts of validity and soundness from their first use in argumentation theory to their understanding in state-of-the-art research. We show how, in the course of this development, dialectical considerations have taken center stage, at the cost of the logical perspective. Then, we take a closer look at the quality dimensions used in the field of computational argument quality assessment. Based on an analysis of prior empirical work in this field, we show how methodological considerations from argument theory can benefit state-of-theart methods in computational argument quality assessment. We propose an even clearer separation between the two quality dimensions not only in regards to their definitions, but also in regards to the granularity at which the argumentative text is being annotated and assessed.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Exploring the Usefulness of Open and Proprietary LLMs in Argumentative Writing Support(2024-07-02); ; ; ; In this article, we present the results of an exploratory study conducted with our self-developed tool Artist. The goal of the tool is to give formative feedback to develop students' argumentation skills. We compare the feedback that two different LLMs, an open-sourced one by META and one of OpenAI's fully proprietary ones, give to students' argumentative writing. We find that, overall, students find the feedback provided by both LLMs helpful (7.51 vs. 7.65 on a scale from 1 to 10), and they rate the quality of the feedback as good to very good. We take this as a very encouraging provisional result that invites larger and more extensive studies on the topic.Type:conference paperScopus© Citations 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluating LLMs' Performance At Automatic Short-Answer Grading(2024-07-08); In recent years, the use of Large Language Models (LLMs) has become more accessible and widespread. With a free-of-charge access types people have began applying the models to various tasks beyond the task of next-word prediction. In an exploratory study, we take a closer look at the use of LLMs for Automatic Short Answer Grading. We compare the grading of short-answer tasks by two human graders to this of an LLM. We discuss the results and present examples of observed shortcomings in the annotation and grading.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Let's discuss! Quality Dimensions and Annotated Datasets for Computational Argument Quality Assessment(Empirical Methods in Natural Language Processing, 2024-11-15); ; Research in the computational assessment of Argumentation Quality has gained popularity over the last ten years. Various quality dimensions have been explored through the creation of domain-specific datasets and assessment methods. We survey the related literature (211 publications and 32 datasets), while addressing potential overlaps and blurry boundaries to related domains. This paper provides a representative overview of the state of the art in Computational Argument Quality Assessment with a focus on annotated datasets. The aim of the survey is to identify research gaps and to aid future discussions and work in the domain.Type:conference paperScopus© Citations 5