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Text generation from graph structure for low-resource languages

Informationen zur Arbeit

Abschlussarbeitstyp: Bachelor, Master
Betreuer: Shuzhou Yuan
Forschungsgruppe: Web Science

Archivierungsnummer: 5024
Abschlussarbeitsstatus: Offen
Beginn: 30. März 2023
Abgabe: unbekannt

Weitere Informationen


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.


• 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.


Ausschreibung: Download (pdf)