Abstract: Systems, devices, and methods of the present invention involve discourse trees. In some aspects, a system creates a discourse tree by identifying elementary discourse units in text. The discourse tree includes nodes, each nonterminal node representing a rhetorical relationship between two elementary discourse units and each terminal node associated with an elementary discourse unit. The system identifies, in a reference sentence of the discourse tree, a rhetorical relation of type elaboration or joint. The system selects a candidate sentence having a highest syntactic generalization score of a set of syntactic generalization scores. The system identifies a semantic relation corresponding to the candidate sentence. The semantic relation corresponds to a word in the candidate sentence and defines a role in the candidate sentence.

Abstract: Systems, devices, and methods of the present invention involve discourse trees. In an example, a method involves generating a discourse tree. The method includes identifying, from the discourse tree, a central entity that is associated with a rhetorical relation of type elaboration and corresponds to a topic node that identifies a central entity of the text. The method includes determining a subset of elementary discourse units of the discourse tree that are associated with the central entity. The method includes forming generalized phrases from the subset of elementary discourse units. The method includes forming tuples from the generalized phrases, where a tuple is an ordered set of words in normal form. The method involves responsive to successfully converting an elementary discourse unit associated with an identified tuple into a logical representation, updating the ontology with an entity from the identified tuple.

Abstract: Techniques for identifying one or more missing fragments within input text are disclosed. A discourse tree (DT) is generated for the input text (IT) received, the IT having any suitable number of sentence fragments. An indication that the IT is likely missing one or more sentence fragments may be identified based on determining that one or more rhetorical relationships of the DT matches one of a set of predefined rhetorical relationships. A query is generated one or more sentence fragments of the IT and executed against a knowledge base to obtain a set of search results. A most-relevance search result can be utilized to identify a set of candidate sentence fragments. A subset of those candidate sentence fragments can be identified based on comparing them to the sentence fragments provided in the IT, each candidate sentence fragment of the subset being implied but excluded from the IT.

Abstract: Disclosed systems relate to generating questions from text. In an example, a method includes forming a first semantic tree from a first reference text and second semantic tree from a second reference text. The method includes identifying a set of semantic nodes that are in the first semantic tree but not in the second semantic tree. The method includes forming a first syntactic tree for the first reference text and a second syntactic tree for the second reference text. The method includes identifying a set of syntactic nodes that are in the first syntactic tree but not in the second syntactic tree. The method includes mapping the set of semantic nodes to the set of syntactic nodes by identifying a correspondence between a semantic node and a syntactic node, forming a question fragment from a normalized word, and providing the question fragment to a user device.

Abstract: Systems, devices, and methods of the present invention involve discourse trees. In an example, a method involves generating a discourse tree. The method includes identifying, from the discourse tree, a central entity that is associated with a rhetorical relation of type elaboration and corresponds to a topic node that identifies a central entity of the text. The method includes determining a subset of elementary discourse units of the discourse tree that are associated with the central entity. The method includes forming generalized phrases from the subset of elementary discourse units. The method includes forming tuples from the generalized phrases, where a tuple is an ordered set of words in normal form. The method involves responsive to successfully converting an elementary discourse unit associated with an identified tuple into a logical representation, updating the ontology with an entity from the identified tuple.

Abstract: Disclosed techniques relate to managing a dialogue between a user device and an autonomous agent. For instance, a computing device creates a discourse tree from a body of text that includes fragments. The discourse tree includes a root node, nonterminal nodes, and terminal nodes. Each nonterminal node represents a rhetorical relationship between two of the fragments and each terminal node is associated with one of the fragments. The computing device determines a main topic of the body of text from the discourse tree. The computing device provides the main topic to the user device, and in response, receives a user utterance. The computing device determines an intent from the user utterance and navigates the discourse tree to locate relevant information consistent with the intent.

Abstract: Techniques are disclosed for identifying hypocrisy in text. A computer system creates, from fragments of text, a syntactic tree that represents syntactic relationships between words in the fragments. The system identifies, in the syntactic tree, a first entity and a second entity. The system further determines that the first entity is opposite to the second entity. The system further determines a first sentiment score for a first fragment comprising the first entity and a second sentiment score for a second fragment comprising the second entity. The system, responsive to determining that the first sentiment score and the second sentiment score indicate opposite emotions, identifies the text as comprising hypocrisy and providing the text to an external device.

Abstract: Systems, devices, and methods of the present invention detect deceptive or fake content in text. In an example, a computer system generates, from text a discourse tree that represents rhetorical relationships between fragments of the text. The computer system generates a communicative discourse tree from the discourse tree. The computer system identifies a number of non-trivial rhetorical relations associated with the nonterminal nodes in the communicated discourse tree and, for each terminal edge having a communicative action, a level of nesting of the communicative action. The computer system derives, from the number of non-trivial rhetorical relations and the levels of nesting of the identified communicative actions, a complexity score that is indicative of a level of deception in the text.

Abstract: Improved techniques for dialogue management are disclosed. In particular, disclosed systems facilitate improved autonomous agents that can generate a virtual social dialogue from a corpus of text. A virtual social dialogue is a dialogue between autonomous agents and user devices. For example, a virtual social dialogue can include viewpoints, concerns, or questions of various actors on a particular topic. By presenting textual content in this manner, disclosed techniques improve information comprehension and increase the practicality of autonomous agents.

Abstract: Systems, devices, and methods discussed herein provide improved autonomous agent applications that are configured to provide explanations in response to user-submitted questions. A user query may be received and a classification (e.g., a general question, a specific question) for the user query may be determined based at least in part on a predefined rule set (or a classification model). A set of decision features (e.g., particular user data) associated with a decision generated by a machine-learning model may be identified. An explanation chain may be identified from a plurality of explanation chains based at least in part on the user query. The explanation chain may describe a logical chain of explanations associated with a decision making process related to the machine-learning model. A response to the user query may be provided based at least in part on the explanation chain and the set of decision features.

Abstract: Systems, devices, and methods discussed herein provide improved decision trees (e.g., supported decision trees). A supported decision tree can be generated by generating discourse trees from various documents associated with a subject. One or more decision chains can be generated from each discourse tree, each decision chain being a sequence of elements comprising a premise and a decision connected by rhetorical relationships. A supported decision tree can be generated from the various decision chains, where the nodes of the decision tree are identified from the elements of the plurality of decision chains and ordered based on a set of predefined priority rules. Subsequent input data can be received and the supported decision tree can be traversed to classify the input data.

Abstract: Techniques are disclosed for generating defeating textual replies. A computer-implemented method generates a first communicative discourse tree from an utterance and as second communicative discourse tree from a candidate answer derived from a search of keywords in the utterance. The method translates the first communicative discourse tree into a first logical formula from and the second communicative discourse tree into a second logical formula. The method determines that the first logical formula is a negation of the second logical formula. The method further provides the candidate answer to a user device responsive to the determining.

Abstract: Systems, devices, and methods discussed herein provide improved autonomous agent applications that are configured to generate automated answers to a question using summarized logical forms (SLFs). A myriad of techniques may be utilized to manually or automatically generate one or more summarized logical forms for an answer, where the summarized logical form(s) identifies the main entities/informative portions of the answer. Instead of indexing the whole of the answer as in conventional methods, an answer can be indexed using the summarized logical forms. A subsequent query may be matched to the SLF and the answer may be provided in response to the question. By indexing the answer with its informative portions, the speed and accuracy of identifying the answer is improved.

Abstract: Systems, devices, and methods of the present invention detect deceptive or fake content in text. In an example, a computer system generates, from text a discourse tree that represents rhetorical relationships between fragments of the text. The computer system generates a communicative discourse tree from the discourse tree. The computer system identifies a number of non-trivial rhetorical relations associated with the nonterminal nodes in the communicated discourse tree and, for each terminal edge having a communicative action, a level of nesting of the communicative action. The computer system derives, from the number of non-trivial rhetorical relations and the levels of nesting of the identified communicative actions, a complexity score that is indicative of a level of deception in the text.

Abstract: A system and method for recommending a product to a user in response to a query for a product with a feature wherein the recommendation is accompanied by a quotation expressing a sentiment about the feature or the product.

Abstract: Disclosed techniques relate to managing a dialogue between a user device and an autonomous agent. For instance, a computing device creates a discourse tree from a body of text that includes fragments. The discourse tree includes a root node, nonterminal nodes, and terminal nodes. Each nonterminal node represents a rhetorical relationship between two of the fragments and each terminal node is associated with one of the fragments. The computing device determines a main topic of the body of text from the discourse tree. The computing device provides the main topic to the user device, and in response, receives a user utterance. The computing device determines an intent from the user utterance and navigates the discourse tree to locate relevant information consistent with the intent.

Abstract: Systems, devices, and methods provide improved autonomous agents by creating a concept lattice that represent objects and attributes and using the concept lattice to manage a dialogue with a user device. An autonomous agent application can receive queries from a user and serve response (e.g., responses identifying objects and/or object attributes) based on one or more traversals of a concept lattice. In some embodiments, the concept lattice can be generated from tabular data indicating a set of objects and respective sets of attributes. The user can provide further input to traverse from one selected node to another within the concept lattice in order to identify other nodes in the concept lattice that meet the user's needs.

Abstract: Navigating text using an extended discourse tree. In an example, a method accesses an extended discourse tree that includes a first discourse tree for a first document and a second discourse tree for a second document. The method determines a first elementary discourse unit that is responsive to a query from a user device and a corresponding first position. The method further determines a set of navigation options including a first rhetorical relationship between the first elementary discourse unit and a second elementary discourse unit of the first discourse tree and a second rhetorical relationship between the first elementary discourse unit and a third elementary discourse unit of the second discourse tree. The method presents the rhetorical relationships to a user device. Responsive to receiving, from a user device, a selection of a rhetorical relationship, the method presents a corresponding elementary discourse unit to the user device.

Abstract: Systems and methods for improving question-answering recall for complex, multi-sentence, convergent questions. More specifically, an autonomous agent accesses an initial answer that partly answers a question received from a user device. The agent represents the question and the initial answer as discourse trees. From the discourse trees, the agent identifies entities in the question that are not addressed by the answer. The agent forms an additional discourse tree from an additional resource such as a corpus of text. The additional discourse tree rhetorically connects a non-addressed entity with the answer. The agent designates this discourse tree as an imaginary discourse tree. When combined with the initial answer discourse tree, the imaginary discourse tree is used to generate an improved answer relative to existing solutions.

Abstract: Systems, devices, and methods of the present invention detect affective argumentation in text. In an example, an application executing on a computing device accesses text comprising fragments. The application creates a discourse tree from the text. The discourse tree includes nodes, each nonterminal node representing a rhetorical relationship between two of the fragments and each terminal node of the nodes of the discourse tree is associated with one of the fragments. The application matches each fragment that has a verb to a verb signature, thereby creating a communicative discourse tree. The application determines whether the communicative discourse tree represents text that includes affective argumentation by applying a classification model trained to detect affective argumentation to the communicative discourse tree.