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arXiv · 2201.03521

Polish Natural Language Inference and Factivity -- an Expert-based Dataset and Benchmarks

Daniel Ziembicki·Anna Wróblewska·Karolina Seweryn
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Abstract

Despite recent breakthroughs in Machine Learning for Natural Language Processing, the Natural Language Inference (NLI) problems still constitute a challenge. To this purpose we contribute a new dataset that focuses exclusively on the factivity phenomenon; however, our task remains the same as other NLI tasks, i.e. prediction of entailment, contradiction or neutral (ECN). The dataset contains entirely natural language utterances in Polish and gathers 2,432 verb-complement pairs and 309 unique verbs. The dataset is based on the National Corpus of Polish (NKJP) and is a representative sample in regards to frequency of main verbs and other linguistic features (e.g. occurrence of internal negation). We found that transformer BERT-based models working on sentences obtained relatively good results ($\approx89\%$ F1 score). Even though better results were achieved using linguistic features ($\approx91\%$ F1 score), this model requires more human labour (humans in the loop) because features were prepared manually by expert linguists. BERT-based models consuming only the input sentences show that they capture most of the complexity of NLI/factivity. Complex cases in the phenomenon - e.g. cases with entitlement (E) and non-factive verbs - remain an open issue for further research.

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Daniel Ziembicki, Anna Wróblewska, Karolina Seweryn. 2022-01-10. Polish Natural Language Inference and Factivity -- an Expert-based Dataset and Benchmarks. https://doi.org/10.1017/s1351324923000220

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