23,022 research outputs found
Multi-turn Inference Matching Network for Natural Language Inference
Natural Language Inference (NLI) is a fundamental and challenging task in
Natural Language Processing (NLP). Most existing methods only apply one-pass
inference process on a mixed matching feature, which is a concatenation of
different matching features between a premise and a hypothesis. In this paper,
we propose a new model called Multi-turn Inference Matching Network (MIMN) to
perform multi-turn inference on different matching features. In each turn, the
model focuses on one particular matching feature instead of the mixed matching
feature. To enhance the interaction between different matching features, a
memory component is employed to store the history inference information. The
inference of each turn is performed on the current matching feature and the
memory. We conduct experiments on three different NLI datasets. The
experimental results show that our model outperforms or achieves the
state-of-the-art performance on all the three datasets
Creativity: Generating Diverse Questions using Variational Autoencoders
Generating diverse questions for given images is an important task for
computational education, entertainment and AI assistants. Different from many
conventional prediction techniques is the need for algorithms to generate a
diverse set of plausible questions, which we refer to as "creativity". In this
paper we propose a creative algorithm for visual question generation which
combines the advantages of variational autoencoders with long short-term memory
networks. We demonstrate that our framework is able to generate a large set of
varying questions given a single input image.Comment: Accepted to CVPR 201
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