Thema5024
Abschlussarbeitstyp: Bachelor, Master
Betreuer: Shuzhou Yuan
Forschungsgruppe: Web Science
Archivierungsnummer: 5024
Abschlussarbeitsstatus: Offen
Beginn:
30. März 2023
Abgabe: unbekannt
Background
Low-resource languages often suffer from a lack of available data and resources, which makes it difficult to develop effective natural language processing (NLP) systems for these languages. However, with the increasing availability of graph-structured data, there is an opportunity to leverage this structure to generate text in low-resource languages.
Previous work on graph-to-text generation has achieved outstanding performance using large language models [1]. However, the ability of these models to generate text in low-resource languages has not been thoroughly studied. The objective of this thesis is to explore the use of graph structure to generate text in low-resource languages. Specifically, the thesis will focus on developing a novel text generation approach that leverages graph-structured data, such as knowledge graphs, to generate high-quality text in low-resource languages.
Prerequisites
• Solid programming skills (e.g. Python).
• Strong interest in natural language processing and machine learning.
• Experience in pre-trained language models or HuggingFace library is a plus.
[1] https://aclanthology.org/2021.nlp4convai-1.20/
Ausschreibung: Download (pdf)