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Found 6 papers, 1 papers with code
Learning to Retrieve for Job Matching
Web-scale search systems typically tackle the scalability challenge with a two-step paradigm: retrieval and ranking.
LinkSAGE: Optimizing Job Matching Using Graph Neural Networks
This methodology decouples the training of the GNN model from that of existing Deep Neural Nets (DNN) models, eliminating the need for frequent GNN retraining while maintaining up-to-date graph signals in near realtime, allowing for the effective integration of GNN insights through transfer learning.
Accurate Supervised and Semi-Supervised Machine Reading for Long Documents
We also evaluate the model in a semi-supervised setting by downsampling the WikiReading training set to create increasingly smaller amounts of supervision, while leaving the full unlabeled document corpus to train a sequence autoencoder on document windows.
Coarse-to-Fine Question Answering for Long Documents
We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models.
Hierarchical Question Answering for Long Documents
We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models.
WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia
The task contains a rich variety of challenging classification and extraction sub-tasks, making it well-suited for end-to-end models such as deep neural networks (DNNs).
