Abstract / Description of output
Dialogue act recognition and simulation are traditionally considered separate processes. Here, we argue that both can be fruitfully treated as interleaved processes within the same probabilistic model, leading to a synchronous improvement of performance in both. To demonstrate this, we train multiple Bayes Nets that predict the timing and content of the next user utterance. A specific focus is on providing support for barge-ins. We describe experiments using the Let's Go data that show an improvement in classification accuracy (+5%) in Bayesian dialogue act recognition involving barge-ins using partial context compared to using full context. Our results also indicate that simulated dialogues with user barge-in are more realistic than simulations without barge-in events.
Original language | English |
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Title of host publication | 2013 IEEE Workshop on Automatic Speech Recognition and Understanding |
Place of Publication | United States |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 102-107 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 9 Jan 2014 |
Event | 2013 IEEE Workshop on Automatic Speech Recognition and Understanding - Olomouc, Czech Republic Duration: 8 Dec 2013 → 12 Dec 2013 https://www.asru2013.org/ |
Workshop
Workshop | 2013 IEEE Workshop on Automatic Speech Recognition and Understanding |
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Abbreviated title | ASRU 2013 |
Country/Territory | Czech Republic |
City | Olomouc |
Period | 8/12/13 → 12/12/13 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- spoken dialogue systems
- dialogue act recognition
- dialogue simulation
- Bayesian nets
- barge-in
- USER SIMULATION