40 citations · 152 across the 36 of their papers we have counts for
8 papers · 1 filter
Analysing Dropout and Compounding Errors in Neural Language Models
James O' Neill, Danushka Bollegala
This paper carries out an empirical analysis of various dropout techniques for language modelling, such as Bernoulli dropout, Gaussian dropout, Curriculum Dropout, Variational Drop…
Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction
Angrosh Mandya, Danushka Bollegala, Frans Coenen +1
We propose in this paper a combined model of Long Short Term Memory and Convolutional Neural Networks (LSTM-CNN) that exploits word embeddings and positional embeddings for cross-s…
Curriculum-Based Neighborhood Sampling For Sequence Prediction
James O' Neill, Danushka Bollegala
The task of multi-step ahead prediction in language models is challenging considering the discrepancy between training and testing. At test time, a language model is required to ma…
Meta-Embedding as Auxiliary Task Regularization
James O' Neill, Danushka Bollegala
Word embeddings have been shown to benefit from ensambling several word embedding sources, often carried out using straightforward mathematical operations over the set of word vect…
Angular-Based Word Meta-Embedding Learning
James O' Neill, Danushka Bollegala
Ensembling word embeddings to improve distributed word representations has shown good success for natural language processing tasks in recent years. These approaches either carry o…
Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by Averaging Source Word Embeddings
Joshua Coates, Danushka Bollegala
Creating accurate meta-embeddings from pre-trained source embeddings has received attention lately. Methods based on global and locally-linear transformation and concatenation have…
ClassiNet -- Predicting Missing Features for Short-Text Classification
Danushka Bollegala, Vincent Atanasov, Takanori Maehara +1
The fundamental problem in short-text classification is \emph{feature sparseness} -- the lack of feature overlap between a trained model and a test instance to be classified. We pr…
Dropping Networks for Transfer Learning
James O' Neill, Danushka Bollegala
Many tasks in natural language understanding require learning relationships between two sequences for various tasks such as natural language inference, paraphrasing and entailment.…