This paper summarises the experimental setup and results of the first shared task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue systems.
#3 best model for Data-to-Text Generation on E2E NLG Challenge
Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods.
SOTA for Data-to-Text Generation on WebNLG
A substantial thread of recent work on latent tree learning has attempted to develop neural network models with parse-valued latent variables and train them on non-parsing tasks, in the hope of having them discover interpretable tree structure.
We aim to automatically generate natural language descriptions about an input structured knowledge base (KB).
In this work, we investigate the task of textual response generation in a multimodal task-oriented dialogue system.
In this paper, we describe DeFactoNLP, the system we designed for the FEVER 2018 Shared Task.