Elena Chistova


2023

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Representation Learning for Hierarchical Classification of Entity Titles
Elena Chistova
Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023)

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End-to-End Argument Mining over Varying Rhetorical Structures
Elena Chistova
Findings of the Association for Computational Linguistics: ACL 2023

Rhetorical Structure Theory implies no single discourse interpretation of a text, and the limitations of RST parsers further exacerbate inconsistent parsing of similar structures. Therefore, it is important to take into account that the same argumentative structure can be found in semantically similar texts with varying rhetorical structures. In this work, the differences between paraphrases within the same argument scheme are evaluated from a rhetorical perspective. The study proposes a deep dependency parsing model to assess the connection between rhetorical and argument structures. The model utilizes rhetorical relations; RST structures of paraphrases serve as training data augmentations. The method allows for end-to-end argumentation analysis using a rhetorical tree instead of a word sequence. It is evaluated on the bilingual Microtexts corpus, and the first results on fully-fledged argument parsing for the Russian version of the corpus are reported. The results suggest that argument mining can benefit from multiple variants of discourse structure.

2019

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Semantic Role Labeling with Pretrained Language Models for Known and Unknown Predicates
Daniil Larionov | Artem Shelmanov | Elena Chistova | Ivan Smirnov
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

We build the first full pipeline for semantic role labelling of Russian texts. The pipeline implements predicate identification, argument extraction, argument classification (labeling), and global scoring via integer linear programming. We train supervised neural network models for argument classification using Russian semantically annotated corpus – FrameBank. However, we note that this resource provides annotations only to a very limited set of predicates. We combat the problem of annotation scarcity by introducing two models that rely on different sets of features: one for “known” predicates that are present in the training set and one for “unknown” predicates that are not. We show that the model for “unknown” predicates can alleviate the lack of annotation by using pretrained embeddings. We perform experiments with various types of embeddings including the ones generated by deep pretrained language models: word2vec, FastText, ELMo, BERT, and show that embeddings generated by deep pretrained language models are superior to classical shallow embeddings for argument classification of both “known” and “unknown” predicates.

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Towards the Data-driven System for Rhetorical Parsing of Russian Texts
Elena Chistova | Maria Kobozeva | Dina Pisarevskaya | Artem Shelmanov | Ivan Smirnov | Svetlana Toldova
Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

Results of the first experimental evaluation of machine learning models trained on Ru-RSTreebank – first Russian corpus annotated within RST framework – are presented. Various lexical, quantitative, morphological, and semantic features were used. In rhetorical relation classification, ensemble of CatBoost model with selected features and a linear SVM model provides the best score (macro F1 = 54.67 ± 0.38). We discover that most of the important features for rhetorical relation classification are related to discourse connectives derived from the connectives lexicon for Russian and from other sources.