In this paper, an important question, whether a small language model can be practically accurate enough, is raised. Afterwards, the purpose of a language model, the problems that a language model faces, and the factor...In this paper, an important question, whether a small language model can be practically accurate enough, is raised. Afterwards, the purpose of a language model, the problems that a language model faces, and the factors that affect the performance of a language model,are analyzed. Finally, a novel method for language model compression is proposed, which makes the large language model usable for applications in handheld devices, such as mobiles, smart phones, personal digital assistants (PDAs), and handheld personal computers (HPCs). In the proposed language model compression method, three aspects are included. First, the language model parameters are analyzed and a criterion based on the importance measure of n-grams is used to determine which n-grams should be kept and which removed. Second, a piecewise linear warping method is proposed to be used to compress the uni-gram count values in the full language model. And third, a rank-based quantization method is adopted to quantize the bi-gram probability values. Experiments show that by using this compression method the language model can be reduced dramatically to only about 1M bytes while the performance almost does not decrease. This provides good evidence that a language model compressed by means of a well-designed compression technique is practically accurate enough, and it makes the language model usable in handheld devices.展开更多
文摘In this paper, an important question, whether a small language model can be practically accurate enough, is raised. Afterwards, the purpose of a language model, the problems that a language model faces, and the factors that affect the performance of a language model,are analyzed. Finally, a novel method for language model compression is proposed, which makes the large language model usable for applications in handheld devices, such as mobiles, smart phones, personal digital assistants (PDAs), and handheld personal computers (HPCs). In the proposed language model compression method, three aspects are included. First, the language model parameters are analyzed and a criterion based on the importance measure of n-grams is used to determine which n-grams should be kept and which removed. Second, a piecewise linear warping method is proposed to be used to compress the uni-gram count values in the full language model. And third, a rank-based quantization method is adopted to quantize the bi-gram probability values. Experiments show that by using this compression method the language model can be reduced dramatically to only about 1M bytes while the performance almost does not decrease. This provides good evidence that a language model compressed by means of a well-designed compression technique is practically accurate enough, and it makes the language model usable in handheld devices.
