343 citations · 894 across the 51 of their papers we have counts for
11 papers · 1 filter
Evaluating the Utility of Hand-crafted Features in Sequence Labelling
Minghao Wu, Fei Liu, Trevor Cohn
Conventional wisdom is that hand-crafted features are redundant for deep learning models, as they already learn adequate representations of text automatically from corpora. In this…
Deep-speare: A Joint Neural Model of Poetic Language, Meter and Rhyme
Jey Han Lau, Trevor Cohn, Timothy Baldwin +2
In this paper, we propose a joint architecture that captures language, rhyme and meter for sonnet modelling. We assess the quality of generated poems using crowd and expert judgeme…
Graph-to-Sequence Learning using Gated Graph Neural Networks
Daniel Beck, Gholamreza Haffari, Trevor Cohn
Many NLP applications can be framed as a graph-to-sequence learning problem. Previous work proposing neural architectures on this setting obtained promising results compared to gra…
A Stochastic Decoder for Neural Machine Translation
Philip Schulz, Wilker Aziz, Trevor Cohn
The process of translation is ambiguous, in that there are typically many valid trans- lations for a given sentence. This gives rise to significant variation in parallel cor- pora,…
Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model
Shivashankar Subramanian, Timothy Baldwin, Trevor Cohn
Online petitions are a cost-effective way for citizens to collectively engage with policy-makers in a democracy. Predicting the popularity of a petition --- commonly measured by it…
Narrative Modeling with Memory Chains and Semantic Supervision
Fei Liu, Trevor Cohn, Timothy Baldwin
Story comprehension requires a deep semantic understanding of the narrative, making it a challenging task. Inspired by previous studies on ROC Story Cloze Test, we propose a novel…
Towards Robust and Privacy-preserving Text Representations
Yitong Li, Timothy Baldwin, Trevor Cohn
Written text often provides sufficient clues to identify the author, their gender, age, and other important attributes. Consequently, the authorship of training and evaluation corp…
What's in a Domain? Learning Domain-Robust Text Representations using Adversarial Training
Yitong Li, Timothy Baldwin, Trevor Cohn
Most real world language problems require learning from heterogenous corpora, raising the problem of learning robust models which generalise well to both similar (in domain) and di…
Semi-supervised User Geolocation via Graph Convolutional Networks
Afshin Rahimi, Trevor Cohn, Timothy Baldwin
Social media user geolocation is vital to many applications such as event detection. In this paper, we propose GCN, a multiview geolocation model based on Graph Convolutional Netwo…
Hierarchical Structured Model for Fine-to-coarse Manifesto Text Analysis
Shivashankar Subramanian, Trevor Cohn, Timothy Baldwin
Election manifestos document the intentions, motives, and views of political parties. They are often used for analysing a party's fine-grained position on a particular issue, as we…
Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-based Sentiment Analysis
Fei Liu, Trevor Cohn, Timothy Baldwin
While neural networks have been shown to achieve impressive results for sentence-level sentiment analysis, targeted aspect-based sentiment analysis (TABSA) --- extraction of fine-g…