End-to-End Representation Learning for Question Answering with Weak Supervision
نویسندگان
چکیده
In this paper we present a factoid question answering system for participation in Task 4 of the QALD-7 shared task. Our system is an end-to-end neural architecture for learning a semantic representation of the input question. It iteratively generates representations and uses a convolutional neural network (CNN) model to score them at each step. We take the semantic representation with the highest final score and execute it against Wikidata to retrieve the answers. We show on the Task 4 data set that our system is able to successfully generalize to new data.
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تاریخ انتشار 2017