Data retrieval using reinforced co-learning for semi-supervised ranking
A computer-implement method comprises: training a classifier with labeled data from a dataset; classifying, by the trained classifier, unlabeled data from the dataset; providing, by the classifier to a policy gradient, a reward signal for each data/query pair; transferring, by the classifier to a ranker, learning; training, by the policy gradient, the ranker; ranking data from the dataset based on a query; and retrieving data from the ranked data in response to the query.
1. A computer-implemented method, comprising:
classifying, by a trained classifier, unlabeled data from a dataset, wherein the classifier includes a neural network;
providing iteratively, by the classifier to a policy gradient function, a reward signal for data/query pairs, wherein the reward signal is a combination of a normalized discounted cumulative gain and a discriminative score output by the classifier;
transferring, by the classifier to a ranker, learning from the classifying, wherein the ranker includes a neural network;
training, by the policy gradient function, the ranker; and
ranking, by the trained ranker, data from the dataset based on a query.
2. The method of claim 1 , wherein the classifier includes feature sharing.
3. The method of claim 2 , wherein the transferred learning includes an intermediate feature.
4. The method of claim 1 , further comprising training the classifier iteratively with labeled data from the dataset.
5. The method of claim 4 , wherein the training the classifier comprises using both positive and negative samples from the dataset.
6. The method of claim 5 , wherein the negative samples include negative classified data.
7. The method of claim 1 , wherein the ranking is further based on predicted relevance based on the query.
8. The method of claim 1 , wherein the ranking the data from the dataset based on the query comprises a Markov Decision Process.
9. The method of claim 1 , further comprising retrieving ranked data from the dataset.
10. A system, comprising:
one or more processors of a machine;
a memory storing instruction that, when executed by the one or more processors, cause the machine to perform operations comprising:
classifying, by a trained classifier, unlabeled data from a dataset, wherein the classifier includes a neural network with feature sharing;
providing iteratively, by the classifier to a policy gradient function, a reward signal for data/query pairs, wherein the reward signal is a combination of a normalized discounted cumulative gain and a discriminative score output by the classifier;
transferring, by the classifier to a ranker, learning from the classifying;
training, by the policy gradient function, the ranker; and
ranking, by the trained ranker, data from the dataset based on a query.
11. The system of claim 10 , wherein the ranker includes a neural network.
12. A machine-readable storage device embodying instructions that, when executed by a machine, cause the machine to perform operations comprising:
classifying, by a trained classifier, unlabeled data from a dataset;
providing iteratively, by the classifier to a policy gradient function, a reward signal for data/query pairs, wherein the reward signal is a combination of a normalized discounted cumulative gain and a discriminative score output by the classifier;
transferring, by the classifier to a ranker, learning from the classifying;
training, by the policy gradient function, the ranker;
ranking, by the trained ranker, data from the dataset based on a query; and
retrieving ranked data from the dataset.
13. The device of claim 12 , wherein the classifier and ranker are neural networks.
14. The device of claim 12 , wherein the classifier is a neural network with feature sharing.
15. The device of claim 14 , wherein the transferred learning includes an intermediate feature.
16. The device of claim 12 , wherein the operations further comprise training the classifier iteratively with labeled data from the dataset.
17. The device of claim 16 , wherein the training the classifier comprises using both positive and negative samples from the dataset.
18. The device of claim 17 , wherein the negative samples include negative classified data.
19. The device of claim 12 , wherein the ranking is further based on predicted relevance based on the query.
20. The device of claim 12 , wherein the ranking the data from the dataset based on the query comprises a Markov Decision Process.