Add to ORKGThis paper presents a method for n-gram language model adaptation based on the principle of minimum discrimination information. A background language model is adapted to fit constraints on its marginal distributions that are derived from new observed data. This work gives a different derivation of the model by Kneser et al. (1997) and extends its application to interpolated language models. The proposed method has been evaluated on an Italian 60K-word broadcast news tas
In this paper, we show how to accommodate a Bayesian variant of Rissanen\u27s MDL into on-line Bayes...
This paper investigates the problem of updating over time the statistical language model (LM) of an ...
In this paper, we present a multi-layer learning approach to the language model (LM) adaptation prob...
This paper presents an unsupervised topic-based language model adaptation method which specializes t...
This paper proposes a novel Language Model (LM) adaptation method based on Minimum Discrimination In...
Stochastic n-gram language models have been successfully applied in continuous speech recognition fo...
Language modeling is critical and indispensable for many natural language ap-plications such as auto...
Topic adaptation for language modeling is concerned with adjusting the probabilities in a language m...
Topic adaptation for language modeling is concerned with ad-justing the probabilities in a language ...
This paper presents two techniques for language model (LM) adaptation. The first aims to build a mor...
Stochastic n-gram language models have been successfully applied in continuous speech recognition fo...
Language modeling is an important part for both speech recognition and machine translation systems. ...
Topic adaptation for language modeling is concerned with adjusting the probabilities in a language ...
International audienceThis paper describes an extension of the n-gram language model: the similar n-...
In this paper, we present novel techniques for performing topic adaptation on an -gram language mode...
In this paper, we show how to accommodate a Bayesian variant of Rissanen\u27s MDL into on-line Bayes...
This paper investigates the problem of updating over time the statistical language model (LM) of an ...
In this paper, we present a multi-layer learning approach to the language model (LM) adaptation prob...
This paper presents an unsupervised topic-based language model adaptation method which specializes t...
This paper proposes a novel Language Model (LM) adaptation method based on Minimum Discrimination In...
Stochastic n-gram language models have been successfully applied in continuous speech recognition fo...
Language modeling is critical and indispensable for many natural language ap-plications such as auto...
Topic adaptation for language modeling is concerned with adjusting the probabilities in a language m...
Topic adaptation for language modeling is concerned with ad-justing the probabilities in a language ...
This paper presents two techniques for language model (LM) adaptation. The first aims to build a mor...
Stochastic n-gram language models have been successfully applied in continuous speech recognition fo...
Language modeling is an important part for both speech recognition and machine translation systems. ...
Topic adaptation for language modeling is concerned with adjusting the probabilities in a language ...
International audienceThis paper describes an extension of the n-gram language model: the similar n-...
In this paper, we present novel techniques for performing topic adaptation on an -gram language mode...
In this paper, we show how to accommodate a Bayesian variant of Rissanen\u27s MDL into on-line Bayes...
This paper investigates the problem of updating over time the statistical language model (LM) of an ...
In this paper, we present a multi-layer learning approach to the language model (LM) adaptation prob...