Lexical and Syntactic Adaptation and Their Impact in Deployed Spoken Dialog Systems
By Svetlana Stoyanchev, Amanda Stent. 2009
57 citations recorded by the source corpus.
In this paper, we examine user adaptation to the system's lexical and syntactic choices in the context of the deployed Let's Go! dialog system. We show that in deployed dialog systems with real users, as in laboratory experiments, users adapt to the system's lexical and syntactic choices. We also show that the system's lexical and syntactic choices, and consequent user adaptation, can have an impact on recognition of task-related concepts. This means that system prompt formulation, even in flexible input dialog systems, can be used to guide users into producing utterances conducive to task success.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ondrej Dusek
- Michael. White
- Soumya Batra
- Ankit Arun
- Shashank Jain
- Peyman Heidari
- Shreyan Bakshi
- Filip Jurcícek
In Natural Language Processing
- Ondrej Dusek
- Michael. White
- Soumya Batra
- Ankit Arun
- Shashank Jain
- Peyman Heidari
- Shreyan Bakshi
- Filip Jurcícek
Citations in the corpus
- Structure-to-Text Generation with Self-Training, Acceptability Classifiers and Context-Conditioning for the GEM Shared Task (2021, background): “…(2005) showed that cache-based language models can be used to adapt NLG systems to align with user’s language, while subsequent work investigated structural priming more specifi-cally (Reitter et al., 2006) and the impact of such adaptation in deployed dialog systems (Stoyanchev and Stent, 2009).”
- A Context-aware Natural Language Generator for Dialogue Systems (2016, background): “slots) and their values (see Fig. 1). NLG is an important component of SDS which has a great impact on the perceived naturalness of the system; its quality can also influence the overall task success (Stoyanchev and Stent, 2009; Lopes et al., 2013). However, typical is there another option inform(lin”
- Entrainment in Spoken Dialogue Systems: Adopting, Predicting and Influencing User Behavior (2013, background): “puters (Coulston et al., 2002; Stoyanchev and Stent, 2009; Bell et al., 2003), it is reasonable for a spoken dialogue system to use this strategy to influence its user to speak in a way that will optimize the performance of its automatic speech recognition (ASR).”