Name
Abstract | ||
|---|---|---|
Application domains for speech-to-speech translation and dialog systems often contain sub-domains and/or task-types for which different outputs are appropriate for a given input. It would be useful to be able to automatically find such subdomain structure in training corpora, and to classify new interactions with the system into one of these sub-domains. To this end. We present a document-clustering approach to such sub-domain classification, which uses a recently-developed algorithm based on von Mises Fisher distributions. We give preliminary perplexity reduction and MT performance results for a speech-to-speech translation system using this model. |
Year | Venue | Keywords |
|---|---|---|
2009 | INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, VOLS 1-5 | speech-to-speech translation, dialog clustering, language model adaptation |
Field | DocType | Citations |
Dialog box,Rule-based machine translation,Example-based machine translation,Computer science,Machine translation,Speech recognition,Speech to speech translation,Computer-assisted translation,Cluster analysis | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Stallard | 1 | 153 | 59.87 |
| Stavros Tsakalidis | 2 | 213 | 13.83 |
| Shirin Saleem | 3 | 78 | 6.61 |