642 citations · 951 across the 7 of their papers we have counts for
9 papers
Adapters for Enhanced Modeling of Multilingual Knowledge and Text
Yifan Hou, Wenxiang Jiao, Meizhen Liu +3
Large language models appear to learn facts from the large text corpora they are trained on. Such facts are encoded implicitly within their many parameters, making it difficult to…
Towards a Theoretical Understanding of Word and Relation Representation
Carl Allen
Representing words by vectors, or embeddings, enables computational reasoning and is foundational to automating natural language tasks. For example, if word embeddings of similar w…
A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning
Carl Allen, Ivana Balažević, Timothy Hospedales
Much progress has been made in semi-supervised learning (SSL) by combining methods that exploit different aspects of the data distribution, e.g. consistency regularisation relies o…
Multi-scale Attributed Node Embedding
Benedek Rozemberczki, Carl Allen, Rik Sarkar
We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an…
Interpreting Knowledge Graph Relation Representation from Word Embeddings
Carl Allen, Ivana Balažević, Timothy Hospedales
Many models learn representations of knowledge graph data by exploiting its low-rank latent structure, encoding known relations between entities and enabling unknown facts to be in…
Analogies Explained: Towards Understanding Word Embeddings
Carl Allen, Timothy Hospedales
Word embeddings generated by neural network methods such as word2vec (W2V) are well known to exhibit seemingly linear behaviour, e.g. the embeddings of analogy "woman is to queen a…
TuckER: Tensor Factorization for Knowledge Graph Completion
Ivana Balažević, Carl Allen, Timothy M. Hospedales
Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferri…
Hypernetwork Knowledge Graph Embeddings
Ivana Balažević, Carl Allen, Timothy M. Hospedales
Knowledge graphs are graphical representations of large databases of facts, which typically suffer from incompleteness. Inferring missing relations (links) between entities (nodes)…
What the Vec? Towards Probabilistically Grounded Embeddings
Carl Allen, Ivana Balažević, Timothy Hospedales
Word2Vec (W2V) and GloVe are popular, fast and efficient word embedding algorithms. Their embeddings are widely used and perform well on a variety of natural language processing ta…