An End-to-End Goal-Oriented Dialog System with a Generative Natural Language Response Generation

Constantin, Stefan; Niehues, Jan; Waibel, Alex

doi:10.48550/arxiv.1803.02279

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such systems give promising results especially for chatting oriented dialogue systems. There is also promising development regarding such dialogue systems in a more goal oriented setting [4] [6].…”

Section: Related Workmentioning

confidence: 99%

“…Since the last couple of years, the frameworks that enable dialogue system development gained popularity. There are now many examples of such frameworks like DialogFlow 5 , Wit.ai 6 and relatively new Amazon Lex 7 . The advantange of these frameworks is that they provide a lot of out-of-the-box features to ease many aspects of dialogue system development such as natural language understanding and dialogue management.…”

Section: Use Case: Generating Intents For Dialogflowmentioning

confidence: 99%

See 1 more Smart Citation

Intent Generation for Goal-Oriented Dialogue Systems based on Schema.org Annotations

Şimşek,

Fensel

2018

Preprint

View full text Add to dashboard Cite

Goal-oriented dialogue systems typically communicate with a backend (e.g. database, Web API) to complete certain tasks to reach a goal. The intents that a dialogue system can recognize are mostly included to the system by the developer statically. For an open dialogue system that can work on more than a small set of well curated data and APIs, this manual intent creation will not scalable. In this paper, we introduce a straightforward methodology for intent creation based on semantic annotation of data and services on the web. With this method, the Natural Language Understanding (NLU) module of a goal-oriented dialogue system can adapt to newly introduced APIs without requiring heavy developer involvement. We were able to extract intents and necessary slots to be filled from schema.org annotations. We were also able to create a set of initial training sentences for classifying user utterances into the generated intents. We demonstrate our approach on the NLU module of a state-of-the art dialogue system development framework.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Use Case: Generating Intents For Dialogflowmentioning

confidence: 99%