LONG-SHORT FIELD MEMORY NETWORKS

1 . A report management system comprising:

a computer system; and

a report manager in the computer system, wherein the report manager is configured:

to identify a subset of data fields for inclusion in a new report;

to determine, by a machine learning model that includes a long-short field memory network, a context of the new report, wherein the context is determined based on the subset and a sequence in which the data fields of the subset were identified;

to determine, by the machine learning model that includes the long-short field memory network, a set of suggested fields based on the context of the new report as determined by the long-short field memory network; and

to display, on a display system, the set of the suggested fields in a graphical user interface on the display system.

2 . The report management system of claim 1 , wherein the subset of data fields includes a title field, a description field, and at least one other field.

3 . The report management system of claim 1 , wherein identifying subset of data fields comprises:

receiving, by the computer system, the subset of data fields in a user input generated by at least one of a human machine interface or artificial intelligence system, wherein the subset is selected from data fields of human resources information generated in providing human resource services.

4 . The report management system of claim 1 , wherein the report manager is further configured:

to identify existing reports and logs for the existing reports, each existing report comprising a selected subset of the data fields and each log comprising a sequence for the selected subset, wherein the logs and the existing reports comprise a training data set; and

to train the machine learning model, including the long-short field memory network, using the training data set, wherein the long-short field memory network is trained to determine the context of the new report and to determine the set of suggested fields based on the log and the context.

5 . The report management system of claim 4 , wherein the machine learning model comprises the long-short field memory network, and generating the set of suggested fields comprises:

predicting, with the long-short field memory network, suggested fields according to the context of the new report;

computing, with a number of fully connected neural networks, a probability density function for each recommended field predicted by the long-short field memory network; and

calculating a weighted average of the probability density functions.

6 . The report management system of claim 5 , wherein displaying the set of the suggested fields comprises:

ranking the set of suggested fields in based on the weighted average of the probability density functions to form a ranked order; and

displaying, on the display system, the set of suggested fields according to the ranked order.

7 . The report management system of claim 1 , wherein the report manager is further configured:

in response to receiving a user input selecting a recommended field, to re-determine, by the machine learning model that includes the long-short field memory network, the context of the new report based on the subset and the sequence including the recommended field;

to determine, by the machine learning model that includes the long-short field memory network, a second set of suggested fields based on the redetermined context of the new report as determined by the long-short field memory network; and

to display, on a display system, the second set of suggested fields in the graphical user interface on the display system.

8 . A method for managing reports, the method comprising:

identifying, by a computer system, a subset of data fields for inclusion in a new report;

determining, by a machine learning model in the computer system that includes a long-short field memory network, a context of the new report, wherein the context is determined based on the subset and a sequence in which the data fields of the subset were identified;

determining, by the machine learning model that includes the long-short field memory network, a set of suggested fields based on the context of the new report as determined by the long-short field memory network; and

displaying, by the computer system on a display system, the set of the suggested fields in a graphical user interface on the display system.

9 . The method of claim 8 , wherein the subset of data fields includes a title field, a description field, and at least one other field.

10 . The method of claim 8 , wherein identifying the subset of data fields comprises:

receiving, by the computer system, the subset of data fields in a user input generated by at least one of a human machine interface or artificial intelligence system, wherein the subset is selected from data fields of human resources information generated in providing human resource services.

11 . The method of claim 8 , further comprising:

identifying, by the computer system, existing reports and logs for the existing reports, each existing report comprising a selected subset of the data fields and each log comprising a sequence for the selected subset, wherein the logs and the existing reports comprise a training data set; and

training, by the computer system, the machine learning model, including the long-short field memory network, using the training data set, wherein the long-short field memory network is trained to determine the context of the new report and to determine the set of suggested fields based on the log and the context.

12 . The method of claim 11 , wherein the machine learning model comprises the long-short field memory network, and generating the set of suggested fields comprises:

predicting, with the long-short field memory network, suggested fields according to the context of the new report;

computing, with a number of fully connected neural networks, a probability density function for each recommended field predicted by the long-short field memory network; and

calculating a weighted average of the probability density functions.

13 . The method of claim 12 , wherein displaying the set of the suggested fields comprises:

ranking, by the computer system, the set of suggested fields in based on the weighted average of the probability density functions to form a ranked order; and

displaying, by the computer system on the display system, the set of suggested fields according to the ranked order.

14 . The method of claim 8 , further comprising:

in response to receiving a user input selecting a recommended field, re-determining, by the machine learning model that includes the long-short field memory network, the context of the new report based on the subset and the sequence including the recommended field;

determining, by the computer system using the machine learning model that includes the long-short field memory network, a second set of suggested fields in response based on the redetermined context of the new report as determined by the long-short field memory network; and

displaying, by the computer system on the display system, the second set of suggested fields in the graphical user interface on the display system.

15 . A computer program product for managing reports, the computer program product comprising:

a computer readable storage media; and

program code, stored on the computer-readable storage media, for identifying a subset of data fields for inclusion in a new report;

program code, stored on the computer-readable storage media, for determining, by a machine learning model that includes a long-short field memory network, a context of the new report, wherein the context is determined based on the subset and a sequence in which the data fields of the subset were identified;

program code, stored on the computer-readable storage media, for determining, by the machine learning model that includes the long-short field memory network, a set of suggested fields based on the context of the new report as determined by the long-short field memory network; and

program code, stored on the computer-readable storage media, for displaying, on a display system, the set of the suggested fields in a graphical user interface on the display system.

16 . The computer program product of claim 15 , wherein the subset of data fields includes a title field, a description field, and at least one other field.

17 . The computer program product of claim 15 , wherein the program code for identifying subset of data fields comprises:

program code for receiving the subset of data fields in a user input generated by at least one of a human machine interface or artificial intelligence system, wherein the subset is selected from data fields of human resources information generated in providing human resource services.

18 . The computer program product of claim 15 , further comprising:

program code, stored on the computer-readable storage media, for identifying existing reports and logs for the existing reports, each existing report comprising a selected subset of the data fields and each log comprising a sequence for the selected subset, wherein the logs and the existing reports comprise a training data set; and

program code, stored on the computer-readable storage media, for training the machine learning model, including the long-short field memory network, using the training data set, wherein the long-short field memory network is trained to determine the context of the new report and to determine the set of suggested fields based on the log and the context.

19 . The computer program product of claim 18 , wherein the machine learning model comprises the long-short field memory network, and the program code for generating the set of suggested fields comprises:

program code for predicting, with the long-short field memory network, suggested fields according to the context of the new report;

program code for computing, with a number of fully connected neural networks, a probability density function for each recommended field predicted by the long-short field memory network; and

program code for calculating a weighted average of the probability density functions.

20 . The computer program product of claim 19 , wherein the program code for displaying the set of the suggested fields comprises:

program code for ranking the set of suggested fields in based on the weighted average of the probability density functions to form a ranked order; and

program code for displaying, on the display system, the set of suggested fields according to the ranked order.

21 . The computer program product of claim 15 , further comprising:

program code, stored on the computer-readable storage media, for re-determining, by the machine learning model that includes the long-short field memory network, in response to receiving a user input selecting a recommended field, to the context of the new report based on the subset and the sequence including the recommended field;

program code, stored on the computer-readable storage media, for determining, by the machine learning model that includes the long-short field memory network, a second set of suggested fields in response based on the redetermined context of the new report as determined by the long-short field memory network; and

program code, stored on the computer-readable storage media, for displaying, on a display system, the second set of suggested fields in the graphical user interface on the display system.