Abbreviations present a significant challenge for NLP systems because they cause tokenization and out-of-vocabulary errors.They can also make the text less readable, especially in reference printed books, where they are extensively used.Abbreviations are especially problematic in low-resource settings, where systems are less robust to begin with.In this paper, we propose a new method for addressing the problems caused by a high density of domainspecific abbreviations in a text.We apply this method to the case of a Slovenian biographical lexicon and evaluate it on a newly developed gold-standard dataset of 51 Slovenian biographies.Our abbreviation identification method performs significantly better than commonly used ad-hoc solutions, especially at identifying unseen abbreviations.We also propose and present the results of a method for expanding the identified abbreviations in context.

Abstract Several metrics have been proposed for assessing the similarity of (abstract) meaning representations (AMRs), but little is known about how they relate to human similarity ratings. Moreover, the current metrics have complementary strengths and weaknesses: Some emphasize speed, while others make the alignment of graph structures explicit, at the price of a costly alignment step. In this work we propose new Weisfeiler-Leman AMR similarity metrics that unify the strengths of previous metrics, while mitigating their weaknesses. Specifically, our new metrics are able to match contextualized substructures and induce n:m alignments between their nodes. Furthermore, we introduce a Benchmark for AMR Metrics based on Overt Objectives (Bamboo), the first benchmark to support empirical assessment of graph-based MR similarity metrics. Bamboo maximizes the interpretability of results by defining multiple overt objectives that range from sentence similarity objectives to stress tests that probe a metric’s robustness against meaning-altering and meaning- preserving graph transformations. We show the benefits of Bamboo by profiling previous metrics and our own metrics. Results indicate that our novel metrics may serve as a strong baseline for future work.

Even though SRL is researched for many languages, major improvements have mostly been obtained for English, for which more resources are available.In fact, existing multilingual SRL datasets contain disparate annotation styles or come from different domains, hampering generalization in multilingual learning.In this work we propose a method to automatically construct an SRL corpus that is parallel in four languages: English, French, German, Spanish, with unified predicate and role annotations that are fully comparable across languages.We apply high-quality machine translation to the English CoNLL-09 dataset and use multilingual BERT to project its highquality annotations to the target languages.We include human-validated test sets that we use to measure the projection quality, and show that projection is denser and more precise than a strong baseline.Finally, we train different SOTA models on our novel corpus for monoand multilingual SRL, showing that the multilingual annotations improve performance especially for the weaker languages.

Angel Daza, Anette Frank. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.