Constructing conclusive answers for autonomous agents

1. A method of computationally fortifying an answer using syntactic parse trees, the method comprising:

accessing a seed sentence comprising a first plurality of text fragments;

obtaining a search result by providing, to a search engine, at least one of the first plurality of text fragments, wherein the search result comprises a second plurality of text fragments;

generating, from the search result, a communicative discourse tree, wherein generating the communicative discourse tree comprises:

creating a discourse tree from the second plurality of text fragments, wherein the discourse tree comprises a plurality of nodes, each nonterminal node representing a rhetorical relationship between two fragments of the second plurality of text fragments and each terminal node of the nodes of the discourse tree is associated with one of the fragments of the second plurality of text fragments; and

matching each fragment that has a verb to a verb signature;

determining whether the search result contains argumentation by providing the communicative discourse tree to a machine learning model trained to detect text comprising argumentation;

responsive to determining that the search result contains argumentation, constructing a paragraph from the search result; and

providing the paragraph to a user device.

2. The method of claim 1 , further comprising training the machine learning model by:

accessing a set of training data comprising a training pair, the training pair comprising a first communicative discourse tree that represents text comprising argumentation and a second communicative discourse tree that represents text without argumentation; and

providing one of the training pairs to the machine learning model;

receiving, from the machine learning model, a determined presence of argumentation;

calculating a loss function by calculating a difference between the determined presence of argumentation and an expected presence of argumentation; and

adjusting internal parameters of the machine learning model to minimize the loss function.

3. The method of claim 1 , wherein obtaining a search result comprises:

identifying an entity within the first plurality of text fragments; and

providing the entity to the search engine.

4. The method of claim 3 , wherein identifying an entity further comprises:

constructing a first syntactic parse tree from the first plurality of text fragments; and

identifying the entity within the first syntactic parse tree.

5. The method of claim 4 , wherein identifying a noun phrase comprises extracting the noun phrase from a node of the first syntactic parse tree.

6. The method of claim 1 , further comprising identifying a relevancy metric based on a common entity between a first syntactic parse tree and a second syntactic parse tree, and wherein providing the paragraph to the user device is based on a determination that the relevancy metric is greater than a threshold.

7. The method of claim 6 , wherein computing the relevancy metric comprises applying an additional a machine learning model to the first syntactic parse tree and the second syntactic parse tree.

8. A system comprising:

a non-transitory computer-readable medium storing computer-executable program instructions; and

a processing device communicatively coupled to the non-transitory computer-readable medium for executing the computer-executable program instructions, wherein executing the computer-executable program instructions configures the processing device to perform operations comprising:

accessing a seed sentence comprising a first plurality of text fragments;

obtaining a search result by providing, to a search engine, at least one of the first plurality of text fragments, wherein the search result comprises a second plurality of text fragments;

generating, from the search result, a communicative discourse tree, wherein generating the communicative discourse tree comprises:

creating a discourse tree from the second plurality of text fragments, wherein the discourse tree comprises a plurality of nodes, each nonterminal node representing a rhetorical relationship between two fragments of the second plurality of text fragments and each terminal node of the nodes of the discourse tree is associated with one of the fragments of the second plurality of text fragments; and

matching each fragment that has a verb to a verb signature;

determining whether the search result contains argumentation by providing the communicative discourse tree to a machine learning model trained to detect text comprising argumentation;

responsive to determining that the search result contains argumentation, constructing a paragraph from the search result; and

providing the paragraph to a user device.

9. The system of claim 8 , wherein executing the computer-executable program instructions configures the processing device to perform operations comprising training the machine learning model by:

accessing a set of training data comprising a training pair, the training pair comprising a first communicative discourse tree that represents text comprising argumentation and a second communicative discourse tree that represents text without argumentation; and

providing one of the training pairs to the machine learning model;

receiving, from the machine learning model, a determined presence of argumentation;

calculating a loss function by calculating a difference between the determined presence of argumentation and an expected presence of argumentation; and

adjusting internal parameters of the machine learning model to minimize the loss function.

10. The system of claim 8 , wherein obtaining a search result comprises:

identifying an entity within the first plurality of text fragments; and

providing the entity to the search engine.

11. The system of claim 10 , wherein identifying an entity further comprises:

constructing a first syntactic parse tree from the first plurality of text fragments; and

identifying the entity within the syntactic parse tree.

12. The system of claim 11 , wherein identifying a noun phrase comprises extracting the noun phrase from a node of the first syntactic parse tree.

13. The system of claim 8 , wherein executing the computer-executable program instructions configures the processing device to perform operations comprising: identifying a relevancy metric based on a common entity between a first syntactic parse tree and a second syntactic parse tree and wherein providing the paragraph to the user device is based on a determination that the relevancy metric is greater than a threshold.

14. The system of claim 13 , wherein computing the relevancy metric comprises applying an additional a machine learning model to the first syntactic parse tree and the second syntactic parse tree.

15. A non-transitory computer-readable storage medium storing computer-executable program instructions, wherein when executed by a processing device, the computer-executable program instructions cause the processing device to perform operations comprising:

accessing a seed sentence comprising a first plurality of text fragments;

obtaining a search result by providing, to a search engine, at least one of the first plurality of text fragments, wherein the search result comprises a second plurality of text fragments;

generating, from the search result, a communicative discourse tree, wherein generating the communicative discourse tree comprises:

creating a discourse tree from the second plurality of text fragments, wherein the discourse tree comprises a plurality of nodes, each nonterminal node representing a rhetorical relationship between two fragments of the second plurality of text fragments and each terminal node of the nodes of the discourse tree is associated with one of the fragments of the second plurality of text fragments; and

matching each fragment that has a verb to a verb signature;

determining whether the search result contains argumentation by providing the communicative discourse tree to a machine learning model trained to detect text comprising argumentation;

responsive to determining that the search result contains argumentation, constructing a paragraph from the search result; and

providing the paragraph to a user device.

16. The storage medium of claim 15 , wherein when executed by a processing device, the computer-executable program instructions cause the processing device to perform operations comprising training the machine learning model by:

accessing a set of training data comprising a training pair, the training pair comprising a first communicative discourse tree that represents text comprising argumentation and a second communicative discourse tree that represents text without argumentation; and

providing one of the training pairs to the machine learning model;

receiving, from the machine learning model, a determined presence of argumentation;

calculating a loss function by calculating a difference between the determined presence of argumentation and an expected presence of argumentation; and

adjusting internal parameters of the machine learning model to minimize the loss function.

17. The storage medium of claim 15 , wherein obtaining a search result comprises:

identifying an entity within the first plurality of text fragments; and

providing the entity to the search engine.

18. The storage medium of claim 17 , wherein identifying an entity further comprises:

constructing a syntactic parse tree from the first plurality of text fragments; and

identifying the entity within the first syntactic parse tree.

19. The storage medium of claim 18 , wherein identifying a noun phrase comprises extracting the noun phrase from a node of the first syntactic parse tree.

20. The storage medium of claim 15 , wherein when executed by a processing device, the computer-executable program instructions cause the processing device to perform operations comprising: identifying a relevancy metric based on a common entity between a first syntactic parse tree and a second syntactic parse tree and wherein providing the paragraph to the user device is based on a determination that the relevancy metric is greater than a threshold.