13 citations · 39 across the 6 of their papers we have counts for
6 papers
Improved Document Modelling with a Neural Discourse Parser
Fajri Koto, Jey Han Lau, Timothy Baldwin
Despite the success of attention-based neural models for natural language generation and classification tasks, they are unable to capture the discourse structure of larger document…
A Joint Model for Multimodal Document Quality Assessment
Aili Shen, Bahar Salehi, Timothy Baldwin +1
The quality of a document is affected by various factors, including grammaticality, readability, stylistics, and expertise depth, making the task of document quality assessment a c…
Automatic Labelling of Topics with Neural Embeddings
Shraey Bhatia, Jey Han Lau, Timothy Baldwin
Topics generated by topic models are typically represented as list of terms. To reduce the cognitive overhead of interpreting these topics for end-users, we propose labelling a top…
Named Entity Recognition for Novel Types by Transfer Learning
Lizhen Qu, Gabriela Ferraro, Liyuan Zhou +2
In named entity recognition, we often don't have a large in-domain training corpus or a knowledge base with adequate coverage to train a model directly. In this paper, we propose a…
Learning Robust Representations of Text
Yitong Li, Trevor Cohn, Timothy Baldwin
Deep neural networks have achieved remarkable results across many language processing tasks, however these methods are highly sensitive to noise and adversarial attacks. We present…
An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation
Jey Han Lau, Timothy Baldwin
Recently, Le and Mikolov (2014) proposed doc2vec as an extension to word2vec (Mikolov et al., 2013a) to learn document-level embeddings. Despite promising results in the original p…