Exploring question understanding and adaptation in neural-network-based question answering
The last several years have seen intensive interest in exploring neural-network-based
models for machine comprehension (MC) and question answering (QA). In this paper, we
approach the problems by closely modelling questions in a neural network framework. We
first introduce syntactic information to help encode questions. We then view and model
different types of questions and the information shared among them as an adaptation task
and proposed adaptation models for them. On the Stanford Question Answering Dataset …
models for machine comprehension (MC) and question answering (QA). In this paper, we
approach the problems by closely modelling questions in a neural network framework. We
first introduce syntactic information to help encode questions. We then view and model
different types of questions and the information shared among them as an adaptation task
and proposed adaptation models for them. On the Stanford Question Answering Dataset …
[CITATION][C] Exploring question understanding and adaptation in neural-network-based question answering. CoRR abs
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