Papers › Event2Mind for Russian: Understanding Emotions and Intents in Texts. Corpus and Model...
Event2Mind for Russian: Understanding Emotions and Intents in Texts. Corpus and Model for Evaluation
Fenogenova A. S., Tikhonova M. I., Filipetskaya D. V., Mironenko F. D., Tabisheva A. O
The paper provides a comprehensive overview of the corpus for the Russian language for the commonsense inference task. Namely, we construct event phrases, which cover a wide range of everyday situations with labelled intents and reactions of the event main participant and emotions of other people involved. The dataset consists of two parts: a crowdsourced corpus of 6,756 examples from Russian sources and a translated into Russian part of the original corpus of 23,409 examples. Apart from this, we use the collected data in order to train the event2mind model for the Russian language. The paper presents careful description of the best Russian model and the results of the conducted experiments.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Common Sense Reasoning | Russian Event2Mind | araneum word2vec (skipgram) + GRU | recall@10 | 0.827 | #1 of 7 | Archive leaderboard | report |
| Common Sense Reasoning | Russian Event2Mind | ruscorpora word2vec (skipgram) + GRU | recall@10 | 0.825 | #2 of 7 | Archive leaderboard | report |
| Common Sense Reasoning | Russian Event2Mind | ruscorpora fasttext + GRU | recall@10 | 0.822 | #3 of 7 | Archive leaderboard | report |
| Common Sense Reasoning | Russian Event2Mind | ruscorpora fasttext + LSTM | recall@10 | 0.821 | #4 of 7 | Archive leaderboard | report |
| Common Sense Reasoning | Russian Event2Mind | araneum fasttext + GRU | recall@10 | 0.819 | #5 of 7 | Archive leaderboard | report |
| Common Sense Reasoning | Russian Event2Mind | araneum fasttext + LSTM | recall@10 | 0.818 | #6 of 7 | Archive leaderboard | report |
| Common Sense Reasoning | Russian Event2Mind | araneum word2vec (skipgram) + LSTM | recall@10 | 0.816 | #7 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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