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Finding the right representations for words is critical for building accurate NLP systems when domain-specific labeled data for the task is scarce. This article investigates novel techniques fo...
Spoken language systems often rely on static speech recognizers. When the underlying models are improved on-the-fly,training is usually performed using unsupervised methods. In this work, we explore a...
In this paper, we propose a method for learning reordering model for BTG-based statistical machine translation (SMT). The model focuses on linguistic features from bilingual phrases. Our method involv...
We consider the problem of learning context-dependent mappings from sentences to logical form. The training examples are sequences of sentences annotated with lambda-calculus meaning representations. ...

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