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In this paper, we introduce a simple unsupervised framework for pre-training hidden-unit conditional random fields (HUCRFs), i.e., learning initial parameter estimates for HUCRFs prior to supervised training.Our framework exploits the model structure of HUCRFs to make effective use of unlabeled data from the same domain or labeled data from a different domain. The key idea is to use the separation...
Personal digital assistants are designed to assist users in easy information retrieval or execute the tasks they are interested in. The conversational medium implies an additional level of intelligence but typically these systems do not support any reference to the user's past interactions. We propose a domain-agnostic approach that enables the system to address queries referring to the past by using...
Human-computer interaction and statistical natural language understanding has changed with the addition of a visual display screen in modern mobile devices, as visual rendering is used to communicate the dialog system's response. Onscreen item identification and resolution when interpreting the user utterances is one critical problem to achieve the natural and accurate human-machine communication...
Spoken language understanding (SLU) systems use various features to detect the domain, intent and semantic slots of a query. In addition to n-grams, features generated from entity dictionaries are often used in model training. Clean or properly weighted dictionaries are critical to improve model's coverage and accuracy for unseen entities during test time. However, clean dictionaries are hard to obtain...
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