Intelligent Query Answering with Sentence Encoding


Intelligent Query Answering with Sentence Encoding – Speech recognition from speech is essential for making better decisions on large-scale medical applications such as speech pathology. In addition to speech recognition, humans have the ability to infer a variety of speech sequences, using semantic descriptors from their speech frames. A common problem associated to such task, as missing word or phrase, is to infer a particular sequence’s semantics during the prediction (or processing). The task naturally requires a robust and accurate speech recognition system to distinguish a given sentence from its context using an efficient and natural approach to the sequence. In this paper, we propose a novel end-to-end neural network-based system that simultaneously learns to infer and predict syntactic relationships between different tokens. We provide theoretical results regarding a novel end-to-end neural network architecture and demonstrate its ability to achieve state-of-the-art end-to-end accuracy, which we call the semantic inference task (MSDA). We also propose an end-to-end training method that can scale to large-scale human-computer interaction research.

This paper presents a general framework for automatic decision making in the context of decision making in dynamic decision contexts. We formalise decision making as a set of distributed decision processes where the agents form their opinions and the actions taken are based on the decision process rules governing the decisions. We apply this framework to a variety of decision processes of non-smooth decision making as well as to decision and resource allocation.

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Intelligent Query Answering with Sentence Encoding

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    Generalist probability theory and dynamic decision support systemsThis paper presents a general framework for automatic decision making in the context of decision making in dynamic decision contexts. We formalise decision making as a set of distributed decision processes where the agents form their opinions and the actions taken are based on the decision process rules governing the decisions. We apply this framework to a variety of decision processes of non-smooth decision making as well as to decision and resource allocation.


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