Data-driven models for timing feedback responses in a Map Task dialogue system
By Raveesh Meena, Gabriel Skantze, Joakim Gustafson. 2014
68 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Language and Linguistics
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Citations in the corpus
- Generative Spoken Dialogue Language Modeling (2022, neutral): “More recently, more sophisticated machine learning-based models of turn-taking have been introduced (Meena et al., 2014; Skantze, 2017; Roddy et al., 2018; Masumura et al., 2018).”
- Explaining Dialogue Evaluation Metrics using Adversarial Behavioral Analysis (2022, background): “The gold standard for evaluation of dialogues is human judgement (Meena et al., 2014; Ultes et al., 2013; Jang et al., 2020; Shim et al., 2021; Khalid et al., 2020b; Pan-fili et al., 2021) but human judgements are hard to obtain.”
- Towards a General, Continuous Model of Turn-taking in Spoken Dialogue using LSTM Recurrent Neural Networks (2017, methodology): “For example, the system should be able to detect whether the user is likely to continue speaking after a brief silence, or whether the system should respond (Meena et al., 2014; Ferrer et al., 2002).”
- Neural Dialogue Context Online End-of-Turn Detection (2018, background): “In human-like spoken dialogue systems, end-of-turn detection that determines whether a target speaker’s utterance is ended or not is an essential technology (Sacks et al., 1974; Meena et al., 2014; Ward and Vault, 2015).”