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  • ソウホテキ バックオフ オ モチイタ ゲンゴ モデル ユウゴウ ツール ノ コウチク
  • Complemental Back-off Algorithm for Merging Language Models

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A new complemental back-off algorithm for merging two N-gram language models is proposed. By merging several topic-dependent or style-dependent models, we can construct a general model that covers wider range of topics easily. However, a conventional method that simply concatenates the training corpora or interpolating each probabilities often levels off the task-dependent characteristics in each language models, and weaken the linguistic constraint in total. We propose a new back-off scheme that assigns the unseen N-gram probabilities according to the probabilities of the another model. It can assign more reliable probabilities to the unseen N-grams, and no original corpora is needed for the merging. We implemented a command tool that realizes this method, and evaluated it on three recognition tasks (medical consulting, food recipe query and newspaper article). The results reveal that our merged model can keep the same accuracy of each original one.


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