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| 语言建模中最小化样本风险算法的研究和改进 |
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袁伟1, 高剑峰2, 步丰林1
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1.上海交通大学,计算机科学与工程系,上海,200230;2.Natural Language Processing Group, Microsoft Research, Redmond 98052,USA
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| 摘要: |
| 目前,一些主流的判别学习算法只能优化光滑可导的损失函数,但在自然语言处理(natural language processing,简称NLP)中,很多应用的直接评价标准(如字符转换错误数(character error rate,简称CER))都是不可导的阶梯形函数.为解决此问题,研究了一种新提出的判别学习算法--最小化样本风险(minimum sample risk,简称MSR)算法.与其他判别训练算法不同,MSR算法直接使用阶梯形函数作为其损失函数.首先,对MSR算法的时空复杂性作了分析和提高;同时,提出了改进的算法MSR-II,使得特征之间相关性的计算更加稳定.此外,还通过大量领域适应性建模实验来考察MSR-II的鲁棒性.日文汉字输入实验的评测结果表明:(1) MSR/MSR-II显著优于传统三元模型,使错误率下降了20.9%;(2) MSR/MSR-II与另两类主流判别学习算法Boosting和Perceptron表现相当;(3) MSR-II不仅在时空复杂度上优于MSR,特征选择的稳定性也更高;(4) 领域适应性建模的结果证明了MSR-II的良好鲁棒性.总之,MSR/MSR-II是一种非常有效的算法.由于其使用的是阶梯形的损失函数,因此可以广泛应用于自然语言处理的各个领域,如拼写校正和机器翻译. |
| 关键词: 语言建模 判别训练算法 输入法编辑器 最小化样本风险 领域适应性建模 |
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| A Study and Improvement of Minimum Sample Risk Methods for Language Modeling |
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YUAN Wei,GAO Jian-Feng,BU Feng-Lin
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| Abstract: |
| Most existing discriminative training methods adopt smooth loss functions that could be optimized directly. In natural language processing (NLP), however, many applications adopt evaluation metrics taking a form as a step function, such as character error rate (CER). To address the problem, a newly-proposed discriminative training method is analyzed, which is called minimum sample risk (MSR). Unlike other discriminative methods, MSR directly takes a step function as its loss function. MSR is firstly analyzed and improved in time/space complexity. Then an improved version MSR-II is proposed, which makes the computation of interference in the step of feature selection more stable. In addition, experiments on domain adaptation are conducted to investigate the robustness of MSR-II. Evaluations on the task of Japanese text input show that: (1) MSR/MSR-II significantly outperforms a traditional trigram model, reducing CER by 20.9%; (2) MSR/MSR-II is comparable to the other two state-of-the-art discriminative algorithms, Boosting and Perceptron; (3) MSR-II outperforms MSR not only in time/space complexity but also in the stability of feature selection; (4) Experimental results of domain adaptation show the robustness of MSR-II. In all, MSR/MSR-II is a quite effective algorithm. Given its step loss function, MSR/MSR-II could be widely applied to many fields of NLP, such as spelling check and machine translation. |
| Key words: language modeling discriminative training method input method editor minimum sample risk domain adaptation modelingv |
