Codebook Design for Speech Guided Car Infotainment Systems
PIT '08 Proceedings of the 4th IEEE tutorial and research workshop on Perception and Interactive Technologies for Speech-Based Systems: Perception in Multimodal Dialogue Systems
To cache or not to cache?: experiments with adaptive models in statistical machine translation
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Context adaptation in statistical machine translation using models with exponentially decaying cache
DANLP 2010 Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
Cache-based document-level statistical machine translation
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
| Hi-index | 0.00 |
Automatic translation from one language to another is a highly ambitious task, and there is already a long history of people trying to solve this problem. Yet there is no answer to this problem, but Statistical Machine Translation (SMT) emerged as a promising candidate and is until now of primary research interest. Language Models are very important for SMT, and this book is suggesting and evaluating techniques to improve language models. An excellent source of inspiration for this is the field of speech recognition. The reason is that language models have been studied thoroughly for speech recognition, where language models play a similar role. However, few of the numerous approaches for speech recognition language models have been tested on SMT. Three different language model techniques are evaluated in this book: class base language models, cache language models and sentence mixture language models. Though this book is primarily geared towards SMT, Students and researchers in all areas of language technologies will find a helpful overview of language model techniques in this book.