Tatyana Ruzsics

Also published as: Tatiana Ruzsics


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Stance Detection in German News Articles
Laura Mascarell | Tatyana Ruzsics | Christian Schneebeli | Philippe Schlattner | Luca Campanella | Severin Klingler | Cristina Kadar
Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)

The widespread use of the Internet and the rapid dissemination of information poses the challenge of identifying the veracity of its content. Stance detection, which is the task of predicting the position of a text in regard to a specific target (e.g. claim or debate question), has been used to determine the veracity of information in tasks such as rumor classification and fake news detection. While most of the work and available datasets for stance detection address short texts snippets extracted from textual dialogues, social media platforms, or news headlines with a strong focus on the English language, there is a lack of resources targeting long texts in other languages. Our contribution in this paper is twofold. First, we present a German dataset of debate questions and news articles that is manually annotated for stance and emotion detection. Second, we leverage the dataset to tackle the supervised task of classifying the stance of a news article with regards to a debate question and provide baseline models as a reference for future work on stance detection in German news articles.

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Interpretability for Morphological Inflection: from Character-level Predictions to Subword-level Rules
Tatyana Ruzsics | Olga Sozinova | Ximena Gutierrez-Vasques | Tanja Samardzic
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Neural models for morphological inflection have recently attained very high results. However, their interpretation remains challenging. Towards this goal, we propose a simple linguistically-motivated variant to the encoder-decoder model with attention. In our model, character-level cross-attention mechanism is complemented with a self-attention module over substrings of the input. We design a novel approach for pattern extraction from attention weights to interpret what the model learn. We apply our methodology to analyze the model’s decisions on three typologically-different languages and find that a) our pattern extraction method applied to cross-attention weights uncovers variation in form of inflection morphemes, b) pattern extraction from self-attention shows triggers for such variation, c) both types of patterns are closely aligned with grammar inflection classes and class assignment criteria, for all three languages. Additionally, we find that the proposed encoder attention component leads to consistent performance improvements over a strong baseline.


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Encoder-Decoder Methods for Text Normalization
Massimo Lusetti | Tatyana Ruzsics | Anne Göhring | Tanja Samardžić | Elisabeth Stark
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

Text normalization is the task of mapping non-canonical language, typical of speech transcription and computer-mediated communication, to a standardized writing. It is an up-stream task necessary to enable the subsequent direct employment of standard natural language processing tools and indispensable for languages such as Swiss German, with strong regional variation and no written standard. Text normalization has been addressed with a variety of methods, most successfully with character-level statistical machine translation (CSMT). In the meantime, machine translation has changed and the new methods, known as neural encoder-decoder (ED) models, resulted in remarkable improvements. Text normalization, however, has not yet followed. A number of neural methods have been tried, but CSMT remains the state-of-the-art. In this work, we normalize Swiss German WhatsApp messages using the ED framework. We exploit the flexibility of this framework, which allows us to learn from the same training data in different ways. In particular, we modify the decoding stage of a plain ED model to include target-side language models operating at different levels of granularity: characters and words. Our systematic comparison shows that our approach results in an improvement over the CSMT state-of-the-art.


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Neural Sequence-to-sequence Learning of Internal Word Structure
Tatyana Ruzsics | Tanja Samardžić
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Learning internal word structure has recently been recognized as an important step in various multilingual processing tasks and in theoretical language comparison. In this paper, we present a neural encoder-decoder model for learning canonical morphological segmentation. Our model combines character-level sequence-to-sequence transformation with a language model over canonical segments. We obtain up to 4% improvement over a strong character-level encoder-decoder baseline for three languages. Our model outperforms the previous state-of-the-art for two languages, while eliminating the need for external resources such as large dictionaries. Finally, by comparing the performance of encoder-decoder and classical statistical machine translation systems trained with and without corpus counts, we show that including corpus counts is beneficial to both approaches.

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Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological Reinflection
Peter Makarov | Tatiana Ruzsics | Simon Clematide
Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection


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A Comparison Between Morphological Complexity Measures: Typological Data vs. Language Corpora
Christian Bentz | Tatyana Ruzsics | Alexander Koplenig | Tanja Samardžić
Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)

Language complexity is an intriguing phenomenon argued to play an important role in both language learning and processing. The need to compare languages with regard to their complexity resulted in a multitude of approaches and methods, ranging from accounts targeting specific structural features to global quantification of variation more generally. In this paper, we investigate the degree to which morphological complexity measures are mutually correlated in a sample of more than 500 languages of 101 language families. We use human expert judgements from the World Atlas of Language Structures (WALS), and compare them to four quantitative measures automatically calculated from language corpora. These consist of three previously defined corpus-derived measures, which are all monolingual, and one new measure based on automatic word-alignment across pairs of languages. We find strong correlations between all the measures, illustrating that both expert judgements and automated approaches converge to similar complexity ratings, and can be used interchangeably.