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
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.
This paper describes the enrichment of WebNLG corpus, with the aim to further extend its usefulness as a resource for evaluating common NLG tasks, including Discourse Ordering, Lexicalization and Referring Expression Generation.
Natural Language Generation plays an important role in the domain of dialogue systems as it determines how users perceive the system.
This taxonomy serves as a reference point to think about how other people should be described, and can be used to classify and compute statistics about labels applied to people.
We present the first multilingual image