Scalable Modified Kneser-Ney Language Model Estimation
نویسندگان
چکیده
We present an efficient algorithm to estimate large modified Kneser-Ney models including interpolation. Streaming and sorting enables the algorithm to scale to much larger models by using a fixed amount of RAM and variable amount of disk. Using one machine with 140 GB RAM for 2.8 days, we built an unpruned model on 126 billion tokens. Machine translation experiments with this model show improvement of 0.8 BLEU point over constrained systems for the 2013 Workshop on Machine Translation task in three language pairs. Our algorithm is also faster for small models: we estimated a model on 302 million tokens using 7.7% of the RAM and 14.0% of the wall time taken by SRILM. The code is open source as part of KenLM.
منابع مشابه
An Empirical Investigation of Discounting in Cross-Domain Language Models
We investigate the empirical behavior of ngram discounts within and across domains. When a language model is trained and evaluated on two corpora from exactly the same domain, discounts are roughly constant, matching the assumptions of modified Kneser-Ney LMs. However, when training and test corpora diverge, the empirical discount grows essentially as a linear function of the n-gram count. We a...
متن کاملA Hierarchical Bayesian Language Model Based On Pitman-Yor Processes
We propose a new hierarchical Bayesian n-gram model of natural languages. Our model makes use of a generalization of the commonly used Dirichlet distributions called Pitman-Yor processes which produce power-law distributions more closely resembling those in natural languages. We show that an approximation to the hierarchical Pitman-Yor language model recovers the exact formulation of interpolat...
متن کاملSub-word Based Language Modeling for Amharic
This paper presents sub-word based language models for Amharic, a morphologically rich and under-resourced language. The language models have been developed (using an open source language modeling toolkit SRILM) with different n-gram order (2 to 5) and smoothing techniques. Among the developed models, the best performing one is a 5gram model with modified Kneser-Ney smoothing and with interpola...
متن کاملLanguage Modeling with Power Low Rank Ensembles
We present power low rank ensembles (PLRE), a flexible framework for n-gram language modeling where ensembles of low rank matrices and tensors are used to obtain smoothed probability estimates of words in context. Our method can be understood as a generalization of ngram modeling to non-integer n, and includes standard techniques such as absolute discounting and Kneser-Ney smoothing as special ...
متن کاملBayesian Language Modelling of German Compounds
In this work we address the challenge of augmenting n-gram language models according to prior linguistic intuitions. We argue that the family of hierarchical Pitman-Yor language models is an attractive vehicle through which to address the problem, and demonstrate the approach by proposing a model for German compounds. In our empirical evaluation the model outperforms a modified Kneser-Ney n-gra...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013