English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

We propose a hierarchically structured reinforcement learning approach toaddress the challenges of planning for generating coherent multi-sentencestories for the visual storytelling task. Within our framework, the task ofgenerating a story given a sequence of images is divided across a two-levelhierarchical decoder. The high-level decoder constructs a plan by generating asemantic concept (i.e., topic) for each image in sequence. The low-leveldecoder generates a sentence for each image using a semantic compositionalnetwork, which effectively grounds the sentence generation conditioned on thetopic. The two decoders are jointly trained end-to-end using reinforcementlearning. We evaluate our model on the visual storytelling (VIST) dataset.Empirical results from both automatic and human evaluations demonstrate thatthe proposed hierarchically structured reinforced training achievessignificantly better performance compared to a strong flat deep reinforcementlearning baseline.

Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation