40,802 research outputs found
Neural topic model with reinforcement learning
In recent years, advances in neural variational inference have achieved many successes in text processing. Examples include neural topic models which are typically built upon variational autoencoder (VAE) with an objective of minimising the error of reconstructing original documents based on the learned latent topic vectors. However, minimising reconstruction errors does not necessarily lead to high quality topics. In this paper, we borrow the idea of reinforcement learning and incorporate topic coherence measures as reward signals to guide the learning of a VAE-based topic model. Furthermore, our proposed model is able to automatically separating background words dynamically from topic words, thus eliminating the pre-processing step of filtering infrequent and/or top frequent words, typically required for learning traditional topic models. Experimental results on the 20 Newsgroups and the NIPS datasets show superior performance both on perplexity and topic coherence measure compared to state-of-the-art neural topic models
Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation
We propose a hierarchically structured reinforcement learning approach to
address the challenges of planning for generating coherent multi-sentence
stories for the visual storytelling task. Within our framework, the task of
generating a story given a sequence of images is divided across a two-level
hierarchical decoder. The high-level decoder constructs a plan by generating a
semantic concept (i.e., topic) for each image in sequence. The low-level
decoder generates a sentence for each image using a semantic compositional
network, which effectively grounds the sentence generation conditioned on the
topic. The two decoders are jointly trained end-to-end using reinforcement
learning. We evaluate our model on the visual storytelling (VIST) dataset.
Empirical results from both automatic and human evaluations demonstrate that
the proposed hierarchically structured reinforced training achieves
significantly better performance compared to a strong flat deep reinforcement
learning baseline.Comment: Accepted to AAAI 201
- …