Combining local and broad topic context to improve term detection

We aim to improve term detection performance by augmenting traditional N-gram language models with multiple levels of topic context. We demonstrate that incorporating complementary aspects of topicality leads to significant improvements in term detection accuracy. We represent broad topic context through document-specific latent topics inferred via a Bayesian topic model. We capture local topic context with a cache-based adaptive language model. Measured on four languages from from the IARPA Babel program, interpolating unigrams from the broad topic context improves term detection performance by up to 1% absolute via lattice re-scoring. Re-decoding with the same document-specific model improves accuracy by up to 2.1%. Adding local context via cached N-grams improves performance by up to 1.6%. A combined approach, re-decoding with latent topic information then re-scoring with the local cached N-grams gives an overall improvement of up to 2.4%. For all languages, combining broad and local topic information outperforms any individual method.