Search Results for author:
Found 12 papers, 8 papers with code
Generationary or ``How We Went beyond Word Sense Inventories and Learned to Gloss''
Mainstream computational lexical semantics embraces the assumption that word senses can be represented as discrete items of a predefined inventory.
Nibbling at the Hard Core of Word Sense Disambiguation
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.
SPRING Goes Online: End-to-End AMR Parsing and Generation
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).
Integrating Personalized PageRank into Neural Word Sense Disambiguation
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.
Cross-Domain Image Captioning with Discriminative Finetuning
However, when the model is used without further tuning to generate captions for out-of-domain datasets, our discriminatively-finetuned captioner generates descriptions that resemble human references more than those produced by the same captioner without finetuning.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Autoregressive Search Engines: Generating Substrings as Document Identifiers
Knowledge-intensive language tasks require NLP systems to both provide the correct answer and retrieve supporting evidence for it in a given corpus.
AAA: Fair Evaluation for Abuse Detection Systems Wanted
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
One SPRING to Rule Them Both: Symmetric AMR Semantic Parsing and Generation without a Complex Pipeline
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 #1 on
AMR-to-Text Generation
on The Little Prince
(BLEURT metric)
Fatality Killed the Cat or: BabelPic, a Multimodal Dataset for Non-Concrete Concepts
Thanks to the wealth of high-quality annotated images available in popular repositories such as ImageNet, multimodal language-vision research is in full bloom.
Breaking Through the 80\% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information
Neural architectures are the current state of the art in Word Sense Disambiguation (WSD).
Ranked #6 on
Word Sense Disambiguation
on Supervised:
Quasi Bidirectional Encoder Representations from Transformers for Word Sense Disambiguation
While contextualized embeddings have produced performance breakthroughs in many Natural Language Processing (NLP) tasks, Word Sense Disambiguation (WSD) has not benefited from them yet.
