In this paper, we present a system for generating semantic representations from Universal Dependencies syntactic parses. The foundation of our pipeline is a rule-based interpretation system, designed to be as universal as possible, which produces the correct semantic structure; the content of this structure can then be filled in by additional (sometimes language-specific) post-processing. The rules which generate semantic resources rely as far as possible on the UD parse alone, so that they can apply to any language for which such a parse can be given (a much larger number than the number of languages for which detailed semantically annotated corpora are available). We discuss our general approach, and highlight areas where the UD annotation scheme makes semantic interpretation less straightforward. We compare our results with the Parallel Meaning Bank, and show that when it comes to modelling semantic structure, our approach shows potential, but also discuss some areas for expansion.

This paper presents a new dataset with Discourse Representation Structures (DRSs) annotated over naturally-occurring sentences. Importantly, these sentences are more varied in length and on average longer than those in the existing gold-standard DRS dataset, the Parallel Meaning Bank, and we show that they are therefore much harder for parsers. We argue, though, that this provides a more realistic assessment of the difficulties of DRS parsing.

This work experiments with various configurations of transformer-based sequence-to-sequence neural networks in training a Discourse Representation Structure (DRS) parser, and presents the results along with the code to reproduce our experiments for use by the community working on DRS parsing. These are configurations that have not been tested in prior work on this task. The Parallel Meaning Bank (PMB) English data sets are used to train the models. The results are evaluated on the PMB test sets using Counter, the standard Evaluation tool for DRSs. We show that the performance improves upon the previous state of the art by 0.5 (F1 %) for PMB 2.2.0 and 1.02 (F1 %) for PMB 3.0.0 test sets. We also present results on PMB 4.0.0, which has not been evaluated using Counter in previous research.

This paper reports on efforts to improve the Oslo-Bergen Tagger for Norwegian morphological tagging. We train two deep neural network-based taggers using the recently introduced Norwegian pre-trained encoder (a BERT model for Norwegian). The first network is a sequence-to-sequence encoder-decoder and the second is a sequence classifier. We test both these configurations in a hybrid system where they combine with the existing rule-based system, and on their own. The sequence-to-sequence system performs better in the hybrid configuration, but the classifier system performs so well that combining it with the rules is actually slightly detrimental to performance.