Nathan Glenn

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This paper investigates semi-supervised methods for discriminative language modeling, whereby n-best lists are “hallucinated” for given reference text and are then used for training n-gram language models using the perceptron algorithm. We perform controlled experiments on a very strong baseline English CTS system, comparing three methods for(More)
Discriminative language modeling is a structured classification problem. Log-linear models have been previously used to address this problem. In this paper, the standard dot-product feature representation used in log-linear models is replaced by a non-linear function parameterized by a neural network. Embeddings are learned for each word and features are(More)
We present our work on semi-supervised learning of discriminative language models where the negative examples for sentences in a text corpus are generated using confusion models for Turkish at various granularities, specifically, word, sub-word, syllable and phone levels. We experiment with different language models and various sampling strategies to select(More)
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Low-height vegetation, common in semiarid regions, is difficult to characterize with airborne LiDAR (light detection and ranging) due to the similarities, in time and space, of the point returns of vegetation and ground. Other complications may occur due to the lowheight vegetation structural characteristics and the effects of terrain slope. This research(More)
The perceptron algorithm was used in [1] to estimate discriminative language models which correct errors in the output of ASR systems. In its simplest version, the algorithm simply increases the weight of n-gram features which appear in the correct (oracle) hypothesis and decreases the weight of n-gram features which appear in the 1-best hypothesis. In this(More)
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