A language-independent representation of meaning is one of the most coveted dreams in Natural Language Understanding.With this goal in mind, several formalisms have been proposed as frameworks for meaning representation in Semantic Parsing.And yet, the dependencies these formalisms share with respect to language-specific repositories of knowledge make the objective of closing the gap between high-and low-resourced languages hard to accomplish.In this paper, we present the Ba-belNet Meaning Representation (BMR), an interlingual formalism that abstracts away from language-specific constraints by taking advantage of the multilingual semantic resources of BabelNet and VerbAtlas.We describe the rationale behind the creation of BMR and put forward BMR 1.0, a dataset labeled entirely according to the new formalism.Moreover, we show how BMR is able to outperform previous formalisms thanks to its fully-semantic framing, which enables top-notch multilingual parsing and generation.We release the code at https: //github.com/SapienzaNLP/bmr.

With state-of-the-art systems having finally attained estimated human performance, Word Sense Disambiguation (WSD) has now joined the array of Natural Language Processing tasks that have seemingly been solved, thanks to the vast amounts of knowledge encoded into Transformer-based pre-trained language models.And yet, if we look below the surface of raw figures, it is easy to realize that current approaches still make trivial mistakes that a human would never make.In this work, we provide evidence showing why the F1 score metric should not simply be taken at face value and present an exhaustive analysis of the errors that seven of the most representative state-of-the-art systems for English all-words WSD make on traditional evaluation benchmarks.In addition, we produce and release a collection of test sets featuring (a) an amended version of the standard evaluation benchmark that fixes its lexical and semantic inaccuracies, (b) 42D, a challenge set devised to assess the resilience of systems with respect to least frequent word senses and senses not seen at training time, and (c) hardEN, a challenge set made up solely of instances which none of the investigated state-of-the-art systems can solve.We make all of the test sets and model predictions available to the research community at https://github.com/ SapienzaNLP/wsd-hard-benchmark.

Mainstream computational lexical semantics embraces the assumption that word senses can be represented as discrete items of a predefined inventory.In this paper we show this needs not be the case, and propose a unified model that is able to produce contextually appropriate definitions.In our model, Generationary, we employ a novel span-based encoding scheme which we use to fine-tune an English pre-trained Encoder-Decoder system to generate glosses.We show that, even though we drop the need of choosing from a predefined sense inventory, our model can be employed effectively: not only does Generationary outperform previous approaches in the generative task of Definition Modeling in many settings, but it also matches or surpasses the state of the art in discriminative tasks such as Word Sense Disambiguation and Word-in-Context.Finally, we show that Generationary benefits from training on data from multiple inventories, with strong gains on various zeroshot benchmarks, including a novel dataset of definitions for free adjective-noun phrases.The software and reproduction materials are available at http://generationary.org.

Marco Maru, Federico Scozzafava, Federico Martelli, Roberto Navigli. 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.