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Pre-trained language models (PLMs) have achieved great success in NLP andhave recently been used for tasks in computational semantics. However, thesetasks do not fully benefit from PLMs since meaning representations are notexplicitly included in the pre-training stage. We introduce multilingualpre-trained language-meaning models based on Discourse RepresentationStructures (DRSs), including meaning representations besides natural languagetexts in the same model, and design a new strategy to reduce the gap betweenthe pre-training and fine-tuning objectives. Since DRSs are language neutral,cross-lingual transfer learning is adopted to further improve the performanceof non-English tasks. Automatic evaluation results show that our approachachieves the best performance on both the multilingual DRS parsing andDRS-to-text generation tasks. Correlation analysis between automatic metricsand human judgements on the generation task further validates the effectivenessof our model. Human inspection reveals that out-of-vocabulary tokens are themain cause of erroneous results.

Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation