In this paper, we present a multi-layer learning approach to the language model (LM) adaptation problem by making use of multi-objective programming (MOP). The overall objective function of conventional MAP-based LM adaptation is im-plicitly a composition of two objective functions: The first objective is concerned with the maximum likelihood estimation of the model parameters from the in-domain data while the second objective is concerned with an appropriate repre-sentation of prior information obtained from a general purpose corpus. In this paper, we separate these individual objective functions, which are at least partially conflicting, and take an MOP approach to LM adaptation. The resulting MOP problem is solved in an iterative manner ...
Progress in natural language processing research is catalyzed by the possibilities given by the wide...
Language models (LMs) are often constructed by building com-ponent models on multiple text sources t...
Stochastic n-gram language models have been successfully applied in continuous speech recognition fo...
This paper presents two techniques for language model (LM) adaptation. The first aims to build a mor...
In this paper we address the issue of building language models for very small training sets by adapt...
Language modeling is critical and indispensable for many natural language ap-plications such as auto...
Building a stochastic language model (LM) for speech recog-nition requires a large corpus of target ...
NLP technologies are uneven for the world's languages as the state-of-the-art models are only availa...
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 a method for n-gram language model adaptation based on the principle of minimum ...
Topic adaptation for language modeling is concerned with adjusting the probabilities in a language ...
Recently there has been a lot of interest in neural network based language models. These models typi...
This paper proposes a novel Language Model (LM) adaptation method based on Minimum Discrimination In...
Language models (LMs) are often constructed by building multiple individual component models that ar...
Progress in natural language processing research is catalyzed by the possibilities given by the wide...
Language models (LMs) are often constructed by building com-ponent models on multiple text sources t...
Stochastic n-gram language models have been successfully applied in continuous speech recognition fo...
This paper presents two techniques for language model (LM) adaptation. The first aims to build a mor...
In this paper we address the issue of building language models for very small training sets by adapt...
Language modeling is critical and indispensable for many natural language ap-plications such as auto...
Building a stochastic language model (LM) for speech recog-nition requires a large corpus of target ...
NLP technologies are uneven for the world's languages as the state-of-the-art models are only availa...
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 a method for n-gram language model adaptation based on the principle of minimum ...
Topic adaptation for language modeling is concerned with adjusting the probabilities in a language ...
Recently there has been a lot of interest in neural network based language models. These models typi...
This paper proposes a novel Language Model (LM) adaptation method based on Minimum Discrimination In...
Language models (LMs) are often constructed by building multiple individual component models that ar...
Progress in natural language processing research is catalyzed by the possibilities given by the wide...
Language models (LMs) are often constructed by building com-ponent models on multiple text sources t...
Stochastic n-gram language models have been successfully applied in continuous speech recognition fo...