40 citations · 152 across the 36 of their papers we have counts for
7 papers · 1 filter
Autoencoding Improves Pre-trained Word Embeddings
Masahiro Kaneko, Danushka Bollegala
Prior work investigating the geometry of pre-trained word embeddings have shown that word embeddings to be distributed in a narrow cone and by centering and projecting using princi…
Spatio-temporal Attention Model for Tactile Texture Recognition
Guanqun Cao, Yi Zhou, Danushka Bollegala +1
Recently, tactile sensing has attracted great interest in robotics, especially for facilitating exploration of unstructured environments and effective manipulation. A detailed unde…
Do not let the history haunt you -- Mitigating Compounding Errors in Conversational Question Answering
Angrosh Mandya, James O'Neill, Danushka Bollegala +1
The Conversational Question Answering (CoQA) task involves answering a sequence of inter-related conversational questions about a contextual paragraph. Although existing approaches…
Weakly-Supervised Neural Response Selection from an Ensemble of Task-Specialised Dialogue Agents
Asir Saeed, Khai Mai, Pham Minh +2
Dialogue engines that incorporate different types of agents to converse with humans are popular. However, conversations are dynamic in the sense that a selected response will chang…
Contextualised Graph Attention for Improved Relation Extraction
Angrosh Mandya, Danushka Bollegala, Frans Coenen
This paper presents a contextualized graph attention network that combines edge features and multiple sub-graphs for improving relation extraction. A novel method is proposed to us…
Multi-source Attention for Unsupervised Domain Adaptation
Xia Cui, Danushka Bollegala
Domain adaptation considers the problem of generalising a model learnt using data from a particular source domain to a different target domain. Often it is difficult to find a suit…
Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction
Danushka Bollegala, Ryuichi Kiryo, Kosuke Tsujino +1
Language-independent tokenisation (LIT) methods that do not require labelled language resources or lexicons have recently gained popularity because of their applicability in resour…