Dialog act modelling for conversational speech

By Andreas Stolcke, Elizabeth Shriberg, Rebecca A. Bates, Noah B. Coccaro, Dan Jurafsky, Rachel Martin, Marie W. Meteer, Klaus Ries, Paul A. Taylor, Carol Van Ess-Dykema. 1998

Subfields: Natural Language Processing

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Citations in the corpus

Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators (2019, methodology): “Starting from a grid representation of the turns of the conversation as a matrix (DAs × entities), these features are designed to capture the patterns of topic and intent shift distribution of a dialog.”
Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion? (2019, background): “Indeed, state-of-the-art speech processing algorithms can infer not only the spoken contents from the speech signal, but also the speaker’s identity [5], intention [6–9], gender [10, 11], emotional state [12–14], pathological condition [15–17], personality [18, 19] and cultural [20, 21] attributes t”
Contextual Topic Modeling For Dialog Systems (2018, methodology): “n data collected during a large-scale chatbot competition known as the AlexaPrize [8]. With the goal of improving the topic model in mind, we train a separate independent model to predict dialog acts [9] in a conversation and observe that incorporating dialog act as an additional feature improves to”
Can Prosody Aid the Automatic Classification of Dialog Acts in Conversational Speech? (1998, methodology): “Our investigation began as part of a larger project (Jurafsky et al., 1997a, 1998b; Stolcke et al., 1998) on DA classification in human-human telephone conversations, using three knowledge sources: (1) a dialog grammar (a statistical model of the sequencing of DAs in a conversation), (2)…”