Classification aware neural topic model for COVID-19 disinformation categorisation

Topic Aware Neural Response Generation

We consider incorporating topic information into a sequenceto-sequence framework to generate informative and interesting responses for chatbots. To this end, we propose a topic aware sequence-to-sequence (TA-Seq2Seq) model. The model utilizes topics to simulate prior human knowledge that guides them to form informative and interesting responses in conversation, and leverages topic information i...

Guided Alignment Training for Topic-Aware Neural Machine Translation

In this paper, we propose an effective way for biasing the attention mechanism of a sequence-to-sequence neural machine translation (NMT) model towards the well-studied statistical word alignment models. We show that our novel guided alignment training approach improves translation quality on real-life ecommerce texts consisting of product titles and descriptions, overcoming the problems posed ...

Explanation of the Coronavirus (COVID-19) Crisis Based on Disinformation Theory: A commentary

A Neural Autoregressive Topic Model

We describe a new model for learning meaningful representations of text documents from an unlabeled collection of documents. This model is inspired by the recently proposed Replicated Softmax, an undirected graphical model of word counts that was shown to learn a better generative model and more meaningful document representations. Specifically, we take inspiration from the conditional mean-fie...

Topic Compositional Neural Language Model

We propose a Topic Compositional Neural Language Model (TCNLM), a novel method designed to simultaneously capture both the global semantic meaning and the local wordordering structure in a document. The TCNLM learns the global semantic coherence of a document via a neural topic model, and the probability of each learned latent topic is further used to build a Mixture-ofExperts (MoE) language mo...