In this paper we present SPRING Online Services, a Web interface and RESTful APIs for our state-of-the-art AMR parsing and generation system, SPRING (Symmetric PaRsIng aNd Generation).
Neural Word Sense Disambiguation (WSD) has recently been shown to benefit from the incorporation of pre-existing knowledge, such as that coming from the WordNet graph.
Mainstream computational lexical semantics embraces the assumption that word senses can be represented as discrete items of a predefined inventory.
In this work, we introduce Adversarial Attacks against Abuse (AAA), a new evaluation strategy and associated metric that better captures a model’s performance on certain classes of hard-to-classify microposts, and for example penalises systems which are biased on low-level lexical features.
Ranked #1 on Hate Speech Detection on Waseem et al., 2018
In Text-to-AMR parsing, current state-of-the-art semantic parsers use cumbersome pipelines integrating several different modules or components, and exploit graph recategorization, i. e., a set of content-specific heuristics that are developed on the basis of the training set.
Ranked #2 on AMR Parsing on LDC2020T02 (using extra training data)
Thanks to the wealth of high-quality annotated images available in popular repositories such as ImageNet, multimodal language-vision research is in full bloom.
Neural architectures are the current state of the art in Word Sense Disambiguation (WSD).
Ranked #6 on Word Sense Disambiguation on Supervised:
While contextualized embeddings have produced performance breakthroughs in many Natural Language Processing (NLP) tasks, Word Sense Disambiguation (WSD) has not benefited from them yet.