Method and apparatus for distribution-based language model adaptation
AssigneeMicrosoft Corporation
InventorsJianfeng Gao, Mingjing Li
Life of this patent
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Abstract
A method and apparatus are provided for adapting a language model to a task-specific domain. Under the method and apparatus, the relative frequency of n-grams in a small training set (i.e. task-specific training data set) and the relative frequency of n-grams in a large training set (i.e. out-of-domain training data set) are used to weight a distribution count of n-grams in the large training set. The weighted distributions are then used to form a modified language model by identifying probabilities for n-grams from the weighted distributions.
Claims14
A method of forming a language model, the method comprising:
- selecting out-of-task training data having n-gram counts;
- selecting task-specific training data having n-gram counts;
- modifying an n-gram count for an n-gram in the out-of-task training data by applying an n-gram-specific weight that is based in part on an n-gram count for the n-gram in the task-specific training data to form modified training data, wherein the n-gram count from the task-specific training data is only used for forming the n-gram-specific weight;
- identifying probabilities for the language model based on the modified training data;
- and
- storing the identified probabilities for the language model.
A computer-readable storage medium having computer-executable instructions for forming a language model through steps comprising:
- determining a count of an entity in a small set of training data;
- changing a count of the entity in a large set of training data based on an entity-specific weight that is formed in part from the count of the entity in the small set of training data to form a modified count of the entity, wherein the count of the entity in the small set of training data is only used for forming the entity-specific weight;
- using the modified count of the entity to identify probabilities for the language model;
- and
- storing the probabilities for the language model.
A method of adapting a general language model to cover a task-specific domain, the method comprising:
- determining a weight based on the relative frequency of an n-gram in the task-specific domain;
- multiplying the weight by a count of the n-gram in a distribution domain associated with the general language model to form a modified count, the modified count formed without adding counts of n-grams in the task-specific domain;
- using only the modified counts of n-grams to determine a probability that forms part of an adapted language model;
- and
- storing the probability as part of the adapted language model.
A computer-readable storage medium having computer-executable components for performing steps comprising:
- selecting out-of-task training data having n-gram counts;
- selecting task-specific training data having n-gram counts;
- modifying an n-gram count for an n-gram in the out-of-task training data by applying an n-gram-specific weight that is based in part on an n-gram count for the n-gram in the task-specific training data to form modified training data, wherein the n-gram count from the task-specific training data is only used for forming the n-gram-specific weight;
- identifying probabilities for the language model based on the modified training data;
- and
- storing the probabilities for the language model.
Dependencies are inferred from the claim text.