Method for follow-up expression for intelligent assistance

1. A method for intelligent assistance, comprising:

identifying one or more insertion points within a natural language input comprising text for providing additional information;

training a backwards language model to predict a previous word given following words in the natural language input;

representing a text expression from the natural language input as a graph;

creating nodes in the graph that represent the additional information;

using the trained backwards language model for: inserting the nodes within the graph, generating and scoring edge weights linking the nodes to the graph, determining node paths within the graph, and determining backwards scores for edges in a back half of the inserted nodes; and

generating a follow-up expression based on the backwards scores and the prediction from the trained backwards language model, the follow-up expression including at least a portion of the natural language input and the additional information at the one or more insertion points for clarifying or supplementing meaning of the natural language input.

2. The method of claim 1 , wherein identifying the one or more insertion points within the natural language input is based on a natural language model, and the backwards language model comprises a neural network.

3. The method of claim 2 , wherein the additional information is determined to be consistent with intent of the natural language input.

4. The method of claim 3 , further comprising:

selecting a set of words from the natural language input while maintaining sequential order of the words present in the natural language input.

5. The method of claim 4 , further comprising:

partitioning the set of words into a first subset and a second subset.

6. The method of claim 5 , further comprising:

determining a word distance between the first subset and the second subset based on the natural language model; and

determining the one or more insertion points based on the word distance.

7. The method of claim 6 , further comprising:

for each insertion point, identifying words to be inserted for forming the additional information, using both a trained forward language model and the trained backwards language model, wherein the trained forward language model predicts probability of a next word given prior words in the natural language input, the trained backwards language model predicts probability of the previous word given following words in the natural language input; and

using the trained forward language model for: inserting the nodes within the graph, generating and scoring of the edge weights, determining of the node paths within the graph, and determining forwards scores for edges in a front half of the inserted nodes, wherein the node paths are further scored with the forwards scores, and generating the follow-up expression is based on the backwards scores, the forward scores, and the prediction from both the trained backwards language model and the trained forward language model.

8. An electronic device comprising:

a memory storing instructions; and

at least one processor executing the instructions including a process configured to:

identify one or more insertion points within a natural language input comprising text for providing additional information;

train a backwards language model to predict a previous word given following words in the natural language input;

represent a text expression from the natural language input as a graph;

create nodes in the graph that represent the additional information;

use the trained backwards language model to: insert the nodes within the graph, generate and score edge weights linking the nodes to the graph, determine node paths within the graph, and determine backwards scores for edges in a back half of the inserted nodes; and

generate a follow-up expression based on the backwards scores and the prediction from the trained backwards language model, the follow-up expression including at least a portion of the natural language input and the additional information at the one or more insertion points for clarifying or supplementing meaning of the natural language input.

9. The electronic device of claim 8 , wherein a natural language model is used to identify the one or more insertion points within the natural language input, the backwards language model comprises a neural network.

10. The electronic device of claim 9 , wherein the additional information is determined to be consistent with intent of the natural language input.

11. The electronic device of claim 10 , wherein the process further comprises:

selection of a set of words from the natural language input while maintaining sequential order of the words present in the natural language input.

12. The electronic device of claim 11 , wherein the process further comprises:

partitioning the set of words into a first subset and a second subset;

determining a word distance between the first subset and the second subset based on the natural language model; and

determining the one or more insertion points based on the word distance.

13. The electronic device of claim 10 , wherein the process is further configured to:

for each insertion point, identify words to be inserted to form the additional information, using both a trained forward language model and the trained backwards language model, wherein the trained forward language model predicts probability of a next word given prior words in the natural language input, and the trained backwards language model predicts probability of the previous word given following words in the natural language input; and

use the trained forward language model to: insert the nodes within the graph, generate and score the edge weights, determine the node paths within the graph, and determine forwards scores for edges in a front half of the inserted nodes, wherein the node paths are further scored with the forwards scores, and generation of the follow-up expression is based on the backwards scores, the forward scores, and the prediction from both the trained backwards language model and the trained forward language model.

14. The electronic device of claim 8 , wherein the process is performed on at least one of a server device, a smart portable device, a smart appliance, or a combination thereof.

15. A non-transitory processor-readable medium that includes a program that when executed by a processor perform a method comprising:

identifying one or more insertion points within a natural language input comprising text for providing additional information;

training a backwards language model to predict a previous word given following words in the natural language input;

representing a text expression from the natural language input as a graph;

creating nodes in the graph that represent the additional information;

using the trained backwards language model for: inserting the nodes within the graph, generating and scoring edge weights linking the nodes to the graph, determining node paths within the graph, and determining backwards scores for edges in a back half of the inserted nodes; and

generating a follow-up expression based on the backwards scores and the prediction from the trained backwards language model, the follow-up expression including at least a portion of the natural language input and the additional information at the one or more insertion points for clarifying or supplementing meaning of the natural language input.

16. The non-transitory processor-readable medium of claim 15 , wherein identifying the one or more insertion points within the natural language input is based on a natural language model, and the backwards language model comprises a neural network.

17. The non-transitory processor-readable medium of claim 16 , wherein the additional information is determined to be consistent with intent of the natural language input.

18. The non-transitory processor-readable medium of claim 17 , wherein the method further comprises:

selecting a set of words from the natural language input while maintaining sequential order of the words present in the natural language input; and

partitioning the set of words into a first subset and a second subset.

19. The non-transitory processor-readable medium of claim 18 , wherein the method further comprises:

determining a word distance between the first subset and the second subset based on the natural language model; and

determining the one or more insertion points based on the word distance.

20. The non-transitory processor-readable medium of claim 19 , wherein the method further comprises:

for each insertion point, identifying words to be inserted for forming the additional information, using both a trained forward language model and the trained backwards language model, wherein the trained forward language model predicts probability of a next word given prior words in the natural language input, and the trained backwards language model predicts probability of the previous word given following words in the natural language input; and

using the trained forward language model for: inserting the nodes within the graph, generating and scoring of the edge weights, determining of the node paths within the graph, and determining forwards scores for edges in a front half of the inserted nodes, wherein the node paths are further scored with the forwards scores, and generating the follow-up expression is based on the backwards scores, the forward scores, and the prediction from both the trained backwards language model and the trained forward language model.