Domain adaptation with clustered language models
By Joerg P. Ueberla. 1997
A method of domain adaptation for clustered language models is developed. It is based on a previously developed clustering algorithm (Ueberla, 1994), but with a modified optimisation criterion. The results are shown to be slightly superior to the previously published 'Fillup' method (Besling and Meier, 1995), which can be used to adapt standard n-gram models. However, the improvement both methods give compared to models built from scratch on the adaptation data is quite small (less than 11% relative improvement in word error rate). This suggests that both methods are still unsatisfactory from a practical point of view.
Subfields: Natural Language Processing, Representation & Self-Supervised Learning
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In Natural Language Processing
- Evangelos Kazakos
- Michael Wray
- Dima Damen
- Giovanni Maria Farinella
- Antonino Furnari
- Toby Perrett
- Hazel Doughty
- Davide Moltisanti
In Representation & Self-Supervised Learning
- Giovanni Maria Farinella
- Antonino Furnari
- Evangelos Kazakos
- Dima Damen
- Jonathan Munro
- Kristen Grauman
- Rohit Girdhar
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