Viktor Hangya

Ludwig-Maximilians-Universität München

I am working in Prof. Dr. Alexander M. Fraser’s group at the Center for Information and Language Processing (CIS) at LMU Munich, Germany. My research focuses on cross-lingual task and machine translation in low resource setups. Previously, I worked on various sentiment analysis tasks in the Natural Language Processing Group at the University of Szeged, Hungary.

Contact Information

Ludwig-Maximilians-Universität München
Centrum für Informations- und Sprachverarbeitung
Viktor Hangya
Oettingenstraße 67
D-80538 München

Room: 126
E-mail: hangyav [-at-] cis.uni-muenchen.de

Publications

  1. Silvia Severini, Viktor Hangya, Alexander Fraser, and Hinrich Schütze. 2020. Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6044–6055. Link
  2. Tobias Eder, Viktor Hangya, and Alexander Fraser. 2020. Anchor-based Bilingual Word Embeddings for Low-Resource Languages. arXiv:2010.12627. Link
  3. Leah Michel, Viktor Hangya, and Alexander Fraser. 2020. Exploring Bilingual Word Embeddings for Hiligaynon, a Low-Resource Language. In Proceedings of The 12th Language Resources and Evaluation Conference, pages 2566–2573. Link
  4. Viktor Hangya and Alexander Fraser. 2020. Towards Handling Compositionality in Low-Resource Bilingual Word Induction. In Proceedings of the 14th Conference of the Association for Machine Translation in the Americas, pages 89–101. Link
  5. Viktor Hangya and Richárd Farkas. 2020. Target-Level Sentiment Analysis on Various Genres. Szte. PhD Thesis. Link
  6. Jindrich Libovicky, Viktor Hangya, Helmut Schmid, and Alexander Fraser. 2020. The LMU Munich System for the WMT20 Very Low Resource Supervised MT Task. In Proceedings ofthe 5th Conference on Machine Translation, pages 1102–1109. Link
  7. Silvia Severini, Viktor Hangya, Alexander Fraser, and Hinrich Schütze. 2020. LMU Bilingual Dictionary Induction System with Word Surface Similarity Scores for BUCC 2020. In Proceedings ofthe 13th Workshop on Building and Using Comparable Corpora, pages 49–55. Link
  8. Dario Stojanovski, Viktor Hangya, Matthias Huck, and Alexander Fraser. 2019. The LMU Munich Unsupervised Machine Translation System for WMT19. In Proceedings of the ACL 2019 Forth Conference on Machine Translation (WMT), pages 592–598. Link
  9. Matthias Huck, Viktor Hangya, and Alexander Fraser. 2019. Better OOV Translation with Bilingual Terminology Mining. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5809–5815. Link
  10. Viktor Hangya and Alexander Fraser. 2019. Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1224–1234. Link
  11. Dario Stojanovski, Viktor Hangya, Matthias Huck, and Alexander Fraser. 2018. The LMU Munich Unsupervised Machine Translation Systems. In Proceedings of the EMNLP 2018 Third Conference on Machine Translation (WMT), pages 513–521.
  12. Viktor Hangya and Alexander Fraser. 2018. An Unsupervised System for Parallel Corpus Filtering. In Proceedings of the EMNLP 2018 Third Conference on Machine Translation (WMT), pages 882–887.
  13. Fabienne Braune, Viktor Hangya, Tobias Eder, and Alexander Fraser. 2018. Evaluating bilingual word embeddings on the long tail. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 188–193. Link
  14. Viktor Hangya, Fabienne Braune, Alexander Fraser, and Hinrich Schütze. 2018. Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 810–820. Link
  15. Viktor Hangya, Fabienne Braune, Yuliya Kalasouskaya, and Alexander Fraser. 2018. Unsupervised Parallel Sentence Extraction from Comparable Corpora. In Proceedings of the 15th International Workshop on Spoken Language Translation (IWSLT), pages 7–13.
  16. Matthias Huck, Dario Stojanovski, Viktor Hangya, and Alexander Fraser. 2018. LMU Munich’s Neural Machine Translation Systems at WMT 2018. In Proceedings of the EMNLP 2018 Third Conference on Machine Translation (WMT), volume 2, pages 648–654.
  17. Viktor Hangya and Richárd Farkas. 2017. A comparative empirical study on social media sentiment analysis over various genres and languages. Artificial Intelligence Review, 47(4):485–505. Link
  18. Viktor Hangya, Zsolt Szántó, and Richárd Farkas. 2017. Latent Syntactic Structure-Based Sentiment Analysis. In Proceeding of the 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA 2017), pages 248–254. Link
  19. Martina Katalin Szabó, Veronika Vincze, Katalin Simkó, Viktor Varga, and Viktor Hangya. 2016. A Hungarian sentiment corpus manually annotated at aspect level. In Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016, pages 2873–2878.
  20. Viktor Hangya. 2015. Automatic Construction of Domain Specific Sentiment Lexicons for Hungarian. In Proceedings of the 18th International Conference on Text, Speech and Dialogue (TSD 2015), pages 201–208. Link
  21. Viktor Hangya, Gábor Berend, István Varga, and Richárd Farkas. 2014. SZTE-NLP: Aspect level opinion mining exploiting syntactic cues. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 610–614. Link
  22. Viktor Hangya and Richárd Farkas. 2013. Filtering and Polarity Detection for Reputation Management on Tweets. In Proceedings of the 4th Conference and Labs of the Evaluation Forum: Working Notes. Link
  23. Viktor Hangya and Richárd Farkas. 2013. Target-oriented opinion mining from tweets. In 4th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2013), pages 251–254. IEEE. Link
  24. Viktor Hangya, Gábor Berend, and Richárd Farkas. 2013. SZTE-NLP: Sentiment Detection on Twitter Messages. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), pages 549–553. Link