About Me

stud.picHi! I’m Matīss. I’m a computer science student in the University of Latvia. My field of study is natural language processing and, in particular, machine translation (MT). The topic of my thesis MT system combination, but apart from that I’ve also done some work on neural MT and multi-word expressions in MT.

In my spare time I ride bicycles, swim, run, take photos, and do small software and hardware hacking projects. I also like technology and gadgets, rock music, Yu-Gi-Oh trading cards, anime and Japan.

On this page I keep and maintain photos that I like the most at the current time. Once or twice per year some get added and others get removed. A wider range of my photos can be found in Keda.


  • Doctorate student in Computing Science, University of Latvia,  2014 – current
  • Master in Computer Science, University of Latvia,  2014
  • Bachelor in Computer Science, University of Latvia, 2012

Research Interests:

  • Natural Language Processing, Computational Linguistics
  • Machine Translation, Hybrid MT, Neural MT
  • Machine Learning, Neural Networks, Sequence-to-Sequence Models


  • 2016
    • M. Rikters. “Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation.” HyTra 6
    • M. Rikters. “Interactive Multi-System Machine Translation with Neural Language Models.” IOS Press Ebook
    • M. Rikters. “Searching for the Best Translation Combination Across All Possible Variants.” Baltic HLT 2016
    • M. Rikters. “K-translate – interactive multi-system machine translation.” Baltic DB&IS 2016
    • M. Rikters, I. Skadiņa. “Combining machine translated sentence chunks from multiple MT systems.” CICLing 2016
    • M. Rikters, I. Skadiņa. “Syntax-based Multi-system Machine Translation.” LREC 2016
  • 2015
    • M. Rikters. “Multi-system machine translation using online APIs for English-Latvian.” HyTra 4

Participation in Projects:

Fun Old Projects:

  • TwitĒdiens – analysis of Latvian tweets about eating and food
  • NLP Tools – a set of my NLP tools and/or demos with links to the related GitHub repositories

Social Networks: