3 citations · 3 across the 3 of their papers we have counts for
4 papers
Deriving Word Vectors from Contextualized Language Models using Topic-Aware Mention Selection
Yixiao Wang, Zied Bouraoui, Luis Espinosa Anke +1
One of the long-standing challenges in lexical semantics consists in learning representations of words which reflect their semantic properties. The remarkable success of word embed…
Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning
Kun Yan, Zied Bouraoui, Ping Wang +2
Few-shot learning (FSL) is the task of learning to recognize previously unseen categories of images from a small number of training examples. This is a challenging task, as the ava…
Few-shot Image Classification with Multi-Facet Prototypes
Kun Yan, Zied Bouraoui, Ping Wang +2
The aim of few-shot learning (FSL) is to learn how to recognize image categories from a small number of training examples. A central challenge is that the available training exampl…
Modelling General Properties of Nouns by Selectively Averaging Contextualised Embeddings
Na Li, Zied Bouraoui, Jose Camacho Collados +3
While the success of pre-trained language models has largely eliminated the need for high-quality static word vectors in many NLP applications, such vectors continue to play an imp…