The latest updates from our Research team.
Four members of our research team spent the past week at the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017) in Copenhagen, Denmark. The conference handbook can be […]
In the last post we looked at how Generative Adversarial Networks could be used to learn representations of documents in an unsupervised manner. In evaluation, we found that although the […]
I presented some preliminary work on using Generative Adversarial Networks to learn distributed representations of documents at the recent NIPS workshop on Adversarial Training. In this post I provide a […]
IntroductionIn this post, AYLIEN NLP Research Intern, Mahdi, talks us through a quick experiment he performed on the back of reading an interesting paper on evolution strategies, by Tim Salimans, […]
Unsupervisedly learned word embeddings have seen tremendous success in numerous NLP tasks in recent years. So much so that in many NLP architectures, they are close to fully replacing more […]
Sentiment analysis is widely used to gauge public opinion towards products, to analyze customer satisfaction, and to detect trends. With the proliferation of customer reviews, more fine-grained aspect-based sentiment analysis […]
There has been a large resurgence of interest in generative models recently (see this blog post by OpenAI for example). These are models that can learn to create data that […]