Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish

Abstract

Negation is a linguistic universal that poses difficulties for cognitive and computational processing. Despite many advances in text analytics, negation resolution remains an acute and continuously researched question in Natural Language Processing. Reliable negation parsing affects results in biomedical text mining, sentiment analysis, machine translation, and many other fields. The availability of multilingual pre-trained general representation models makes it possible to experiment with negation detection in languages that lack annotated data. In this work we test the performance of two state-of-the-art contextual representation models, Multilingual BERT and XLM-RoBERTa. We resolve negation scope by conducting zero-shot transfer between English, Spanish, French, and Russian. Our best result amounts to a token-level F1-score of 86.86% between Spanish and Russian. We correlate these results with a linguistic negation typology and lexical capacity of the models.