3 |
Bootstrapping Multilingual Intent Models via Machine Translation for Dialog Automation ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Bootstrapping Multilingual Intent Models via Machine Translation for Dialog Automation
|
|
|
|
BASE
|
|
Show details
|
|
16 |
MODELING THE INTONATION OF DISCOURSE SEGMENTS FOR IMPROVED ONLINE DIALOG ACT TAGGING
|
|
|
|
Abstract:
Prosody is an important cue for identifying dialog acts. In this paper, we show that modeling the sequence of acoustic-prosodic values as n-gram features with a maximum entropy model for dialog act (DA) tagging can perform better than conventional approaches that use coarse representation of the prosodic contour through acoustic correlates of prosody. We also propose a discriminative framework that exploits preceding context in the form of lexical and prosodic cues from previous discourse segments. Such a scheme facilitates online DA tagging and offers robustness in the decoding process, unlike greedy decoding schemes that can potentially propagate errors. Using only lexical and prosodic cues from 3 previous utterances, we achieve a DA tagging accuracy of 72% compared to the best case scenario with accurate knowledge of previous DA tag, which results in 74% accuracy.
|
|
Keyword:
Article
|
|
URL: http://www.ncbi.nlm.nih.gov/pubmed/19132136 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2614672 https://doi.org/10.1109/ICASSP.2008.4518789
|
|
BASE
|
|
Hide details
|
|
17 |
Exploiting Acoustic and Syntactic Features for Automatic Prosody Labeling in a Maximum Entropy Framework
|
|
|
|
BASE
|
|
Show details
|
|
|
|