Zsolt Szántó


ProsperAMnet at the FinSim Task: Detecting hypernyms of financial concepts via measuring the information stored in sparse word representations
Gábor Berend | Norbert Kis-Szabó | Zsolt Szántó
Proceedings of the Second Workshop on Financial Technology and Natural Language Processing

ProsperAMnet at FinCausal 2020, Task 1 & 2: Modeling causality in financial texts using multi-headed transformers
Zsolt Szántó | Gábor Berend
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation

This paper introduces our efforts at the FinCasual shared task for modeling causality in financial utterances. Our approach uses the commonly and successfully applied strategy of fine-tuning a transformer-based language model with a twist, i.e. we modified the training and inference mechanism such that our model produces multiple predictions for the same instance. By designing such a model that returns k>1 predictions at the same time, we not only obtain a more resource efficient training (as opposed to fine-tuning some pre-trained language model k independent times), but our results indicate that we are also capable of obtaining comparable or even better evaluation scores that way. We compare multiple strategies for combining the k predictions of our model. Our submissions got ranked third on both subtasks of the shared task.


Universal Dependencies and Morphology for Hungarian - and on the Price of Universality
Veronika Vincze | Katalin Simkó | Zsolt Szántó | Richárd Farkas
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

In this paper, we present how the principles of universal dependencies and morphology have been adapted to Hungarian. We report the most challenging grammatical phenomena and our solutions to those. On the basis of the adapted guidelines, we have converted and manually corrected 1,800 sentences from the Szeged Treebank to universal dependency format. We also introduce experiments on this manually annotated corpus for evaluating automatic conversion and the added value of language-specific, i.e. non-universal, annotations. Our results reveal that converting to universal dependencies is not necessarily trivial, moreover, using language-specific morphological features may have an impact on overall performance.


Introducing the IMS-Wrocław-Szeged-CIS entry at the SPMRL 2014 Shared Task: Reranking and Morpho-syntax meet Unlabeled Data
Anders Björkelund | Özlem Çetinoğlu | Agnieszka Faleńska | Richárd Farkas | Thomas Mueller | Wolfgang Seeker | Zsolt Szántó
Proceedings of the First Joint Workshop on Statistical Parsing of Morphologically Rich Languages and Syntactic Analysis of Non-Canonical Languages

An Empirical Evaluation of Automatic Conversion from Constituency to Dependency in Hungarian
Katalin Ilona Simkó | Veronika Vincze | Zsolt Szántó | Richárd Farkas
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

Special Techniques for Constituent Parsing of Morphologically Rich Languages
Zsolt Szántó | Richárd Farkas
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics