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An Inference Approach To Question Answering Over Knowledge Graphs
The query conversion models and direct models both require specific training data pertaining to the domain of the knowledge graph.
Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing
In this work, we propose to enhance learning models with world knowledge in the form of Knowledge Graph (KG) fact triples for Natural Language Processing (NLP) tasks.
Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing
With our proposed cross dataset learning procedure we show that one can achieve competitive/better performance than learning from a single dataset.
A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference
In this work we use the recent advances in representation learning to propose a neural architecture for the problem of natural language inference.
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