Answer generation using machine reading comprehension and supported decision trees

Patent metadata

Field	Value
Publication number	US-12430327-B2
Application number	US-202217843845-A
Country	US
Kind code	B2
Filing date	Jun 17, 2022
Priority date	Jul 28, 2021
Publication date	Sep 30, 2025
Grant date	Sep 30, 2025

Abstract

Systems, devices, and methods discussed herein are directed to generating an answer to an input query using machine reading comprehension techniques and a lattice of supported decision trees. A supported decision tree can be generated from the various decision chains (e.g., a sequence of elements comprising a premise and a decision connected by rhetorical relationships), where the nodes of the decision tree are identified from the plurality of decision chains and ordered based on a set of predefined priority rules. A lattice may include nodes that individually correspond to a respective supported decision tree. Nodes of the lattice may be identified for an input query. The passages corresponding to those nodes may be obtained and an answer for the query may be generated from the obtained passages using machine reading comprehension techniques. The generated answer may be provided in response to the query.

First claim

What is claimed is:

1. A method for generating an answer to an input query using machine reading comprehension techniques and a lattice of supported decision trees, the method comprising:
receiving a query as input;
accessing the lattice of a plurality of supported decision trees generated from a corpus of documents individually comprising a plurality of passages, the lattice being previously generated and comprising a plurality of nodes, each of the plurality of nodes represented by supported decision trees of the plurality of supported decision trees, each of the supported decision trees having a plurality of paths individually corresponding to a passage of the plurality of passages, wherein the plurality of supported decision trees are generated by:
generating a first plurality of decision chains and a second plurality of decision chains from the corpus of documents;
identifying, based at least in part on a predefined ontology, a common entity between the first plurality of decision chains and the second plurality of decision chains; and
generating the plurality of supported decision trees based on the first plurality of decision chains, the second plurality of decision chains, and the common entity;

identifying, based on the query, one or more nodes of one or more supported decision trees of the lattice;
obtaining one or more passages from the plurality of passages based on the one or more nodes identified from the one or more supported decision trees of the lattice;
generating, utilizing the machine reading comprehension techniques, a corresponding answer to the query based on the one or more passages obtained based on the one or more nodes identified from the one or more supported decision trees of the lattice; and
providing the answer in response to the query via a user interface (UI) of a computer device.

2. The method of claim 1 , wherein the plurality of supported decision trees are further generated based at least in part on:
generating a first discourse tree from a first document of the corpus of documents and a second discourse tree from a second document of the corpus of documents, each discourse tree including a respective plurality of nodes, each nonterminal node representing a rhetorical relationship between at least two fragments of a corresponding document, and each terminal node of the nodes of the discourse tree being associated with one of the fragments, the first and second documents from the corpus of documents;
generating the first plurality of decision chains from the first discourse tree and the second plurality of decision chains from the second discourse tree, each decision chain being a sequence of elements comprising a premise and a decision connected by rhetorical relationships, the elements being identified from the plurality of nodes of the discourse trees; and
generating a corresponding supported decision tree based at least in part on the first and second plurality of decision chains, the corresponding supported decision tree having nodes that correspond to a feature of a decision and edges corresponding to a value of the feature, wherein the nodes of the corresponding supported decision tree are identified from the elements of the plurality of decision chains and ordered based at least in part on a set of predefined priority rules.

3. The method of claim 2 , further comprising:
identifying a respective premise and corresponding decision from the first discourse tree based at least in part on the rhetorical relationships identified by the nodes of the first discourse tree; and
generating a decision chain to comprise the respective premise and the corresponding decision.

4. The method of claim 2 , further comprising:
identifying, based at least in part on the predefined ontology, the common entity of two decision chains, wherein a first of the two decision chains is included in the first plurality of decision chains and a second of the two decision chains is included in the second plurality of decision chains; and
merging the two decision chains to form a decision navigation graph, the two decision chains being merged based at least in part on the common entity, the decision navigation graph comprising nodes representing each respective element of the two decision chains connected by edges representing the rhetorical relationships.

5. The method of claim 4 , further comprising:
ordering the nodes of the decision navigation graph to form a first decision pre-tree, the first decision pre-tree being a fragment of the corresponding supported decision tree, the ordering being performed in accordance with a set of predefined priority rules;
ordering the nodes of the decision navigation graph to form a second decision pre-tree, the second decision pre-tree being a second fragment of the corresponding supported decision tree;
assigning linguistic information comprising an entity type, one or more entity attributes, and one or more rhetorical relationships to each node of the first decision pre-tree and second decision pre-tree; and
merging the first decision pre-tree and the second decision pre-tree to form the corresponding supported decision tree.

6. The method of claim 1 , wherein the lattice of the plurality of supported decision trees is generated from the corpus of documents based on identifying shared attributes associated with each of a subset of the plurality of supported decision trees.

7. The method of claim 1 , further comprising maintaining a mapping between a set of passages to nodes of a given supported decision tree, wherein the mapping is utilized to obtain the one or more passages from the plurality of passages based on the one or more nodes identified from the one or more supported decision trees of the lattice.

8. A computing device, comprising:
one or more processors; and
one or more memories storing computer-readable instructions for generating an answer to an input query using machine reading comprehension techniques and a lattice of supported decision trees, that, when executed by the one or more processors, cause the computing device to perform operations comprising:
receiving a query as input;
accessing the lattice of a plurality of supported decision trees generated from a corpus of documents individually comprising a plurality of passages, the lattice being previously generated and comprising a plurality of nodes, each of the plurality of nodes represented by supported decision trees of the plurality of supported decision trees, each of the supported decision trees having a plurality of paths individually corresponding to a passage of the plurality of passages, wherein the plurality of supported decision trees are generated by:
generating a first plurality of decision chains and a second plurality of decision chains from the corpus of documents;
identifying, based at least in part on a predefined ontology, a common entity between the first plurality of decision chains and the second plurality of decision chains; and
generating the plurality of supported decision trees based on the first plurality of decision chains, the second plurality of decision chains, and the common entity;

identifying, based on the query, one or more nodes of one or more supported decision trees of the lattice;
obtaining one or more passages from the plurality of passages based on the one or more nodes identified from the one or more supported decision trees of the lattice;
generating, utilizing the machine reading comprehension techniques, a corresponding answer to the query based on the one or more passages obtained based on the one or more nodes identified from the one or more supported decision trees of the lattice; and
providing the answer in response to the query via a user interface (UI) of

Assignees

• Oracle Int Corp

Inventors

• Galitsky Boris

Classifications

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06F16/288

  Entity relationship models · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F16/243 Primary

  Natural language query formulation · CPC title

• G06F16/3329 Primary

  Natural language query formulation · CPC title