MSc Defences November/December

See the list of MSc defences at DIKU this fall/winter. The list will continuously be updated.

If the defences are announced as ‘online defence’, the student has to be alone in the room during the examination and assessment. Guests can participate online but the links for the defences are not public. If you want to be present during the defence, please contact or the supervisor for a link.

IT & Cognition



Encoding Representation for machine translation


Algorithms that operate directly over meaning representations are becoming more popular in the field of Natural Language Processing. Most of current research has focused on developing new graph based algorithms for Natural Language Processing applications such as semantic role labeling, text classification and text generation. In this thesis I focus on expanding our understanding of the capabilities of this innovative family of algorithms.

To do so, in this thesis I apply Graph Convolutional Network (GCN) encoders to generate translation where the input is a semantic or a syntactic representation. While this has been done in the past, in
this project I aim to expand our knowledge of this model on two axes. First I use a challenge set approach to asses whether or not graph-based algorithms perform better than transformer machine translation systems in the context of Long Distance Dependencies. Then I dive into the application of graph convolutional network on low-resource machine translation.

I show that GCN encoders are able to boost the performance of machine translation systems trained over relative small number of examples. Then I present results that show that Long Distance Dependencies are also a challenge for GCN based machine translation systems, where only in some
cases GCN models outperform transformer based systems on the challenge sets that I utilize.


Daniel Hershcovich

External examiner(s)

Peter Dolog

Time and place

26 November 09:30