M.Sc. Amy Isard

@conference{ead4dc043ec84bc6aad1c257845465ca,

title = "Digital Edition of the Levezow Album: Interactive Visualization of 17th-Century Drawings",

abstract = "The {"}Digital Edition Levezow Album{"} project is an interdisciplinary collaboration between the Hub of Computing and Data Science (HCDS), the Department of Art History at the University of Hamburg, and the State and University Library Hamburg. The project aims to digitally process and interactively visualize a previously unexplored sketchbook from the late 17th century, containing drawings on anatomy, antiquity, proportion studies, and natural history.By leveraging modern technologies such as digital editing techniques and advanced image processing, the Levezow Album is made accessible to a broad audience. Each page of the album is accompanied by detailed explanations authored by students of the Department of Art History. These texts provide context regarding the significance, origins, and intricacies of the drawings. Additionally, an interactive commenting feature allows users to suggest alternative sources and engage in a dialogue about the artworks.This project demonstrates how digital methods can be used in the humanities to reinterpret and make historical artifacts accessible. It serves as an example of the successful integration of research, education, and digital technology to promote cultural heritage.",

keywords = "Digital Edition, art history, interactive visualization, Digital Humanities, Cultural Heritage",

author = "Amy Isard and Janis-Marie Paul and {von Waldow}, Lina and Anne Liewert and Iris Wenderholm and Martin Semmann",

year = "2025",

language = "English",

note = "deRSE25 - Conference for Research Software Engineering in Germany, deRSE ; Conference date: 25-02-2025 Through 27-02-2025",

url = "https://events.hifis.net/event/1741/",

@conference{b10edd1b70ac4b5cb2a3b8e6d53c1419,

title = "Curation Criteria for Multimodal and Multilingual Data: a Mixed Study within the QUEST Project.",

abstract = "We conducted a user survey and expert interviews within the ongoing QUEST project to get animpression of the needs of users and researchers who are working with multimodal and multi-lingual linguistic corpora. This contribution describes the design and results of the mixed study,whose main goal is to improve the reuse potential of these resources, and to identify concretetopics which are important for the curation of such data.",

author = "Amy Isard and Elena Arestau",

note = "DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.",

year = "2022",

month = jul,

day = "8",

doi = "10.3384/ECP1896",

language = "English",

pages = "56--67",

@conference{9dfec6e6f3a746eba9a689acd140a834,

title = "Neural NLG for Methodius: From RST Meaning Representations to Texts",

abstract = "While classic NLG systems typically made use of hierarchically structured content plans that included discourse relations as central components, more recent neural approaches have mostly mapped simple, flat inputs to texts without representing discourse relations explicitly. In this paper, we investigate whether it is beneficial to include discourse relations in the input to neural data-to-text generators for texts where discourse relations play an important role. To do so, we reimplement the sentence planning and realization components of a classic NLG system, Methodius, using LSTM sequence-to-sequence (seq2seq) models. We find that although seq2seq models can learn to generate fluent and grammatical texts remarkably well with sufficiently representative Methodius training data, they cannot learn to correctly express Methodius's SIMILARITY and CONTRAST comparisons unless the corresponding RST relations are included in the inputs. Additionally, we experiment with using self-training and reverse model reranking to better handle train/test data mismatches, and find that while these methods help reduce content errors, it remains essential to include discourse relations in the input to obtain optimal performance.",

author = "Stevens-Guille, {Symon Jory} and Aleksandre Maskharashvili and Amy Isard and Xintong Li and Michael White",

note = "Publisher Copyright: {\textcopyright} 2020 Association for Computational Linguistics; 13th International Conference on Natural Language Generation, INLG 2020 ; Conference date: 15-12-2020 Through 18-12-2020",

year = "2020",

doi = "10.18653/v1/2020.inlg-1.37",

language = "English",

pages = "306--315",