Abstract: A system and method of determining whether specific clauses in a contract have a likelihood of falling under a particular risk category is disclosed. The proposed systems and methods describe an autonomous digital assistant that accepts contract-related queries from end-users and generates conversational responses. For example, an end-user may request information about the contract's terms, and the system can automatically provide intelligent analytical responses that bypass the need to manually evaluate the various features of the document.

Abstract: Examples of a textual entailment generation system are provided. The system obtains a query from a user and implements an artificial intelligence component to identify a premise, a word index, and a premise index associated with the query. The system may implement a first cognitive learning operation to determine a plurality of hypothesis and a hypothesis index corresponding to the premise. The system may generate a confidence index for each of the plurality of hypothesis based on a comparison of the hypothesis index with the premise index. The system may determine an entailment value, a contradiction value, and a neutral entailment value based on the confidence index for each of the plurality of hypothesis. The system may generate an entailment result relevant for resolving the query comprising the plurality of hypothesis along with the corresponding entailed output index.

Abstract: An automatic question answering system serves to respond to questions pertaining to a user-selected domain of a plurality of domains. A plurality of documents pertaining to the domain are received and processed to identify candidate answers. Questions corresponding to the candidate answers are automatically generated. The candidate answers and the questions are used to train a machine comprehension (MC) model. A user interface allows the selection of one of the plurality of domains for querying by a user. When a user query pertaining to the selected domain is received, the user query is analyzed to identify a relevant context from the plurality of documents. An answer span to the user query identified by the MC model from the relevant context is used to frame a complete response to the user query.

Abstract: A system and method of determining whether specific clauses in a contract have a likelihood of falling under a particular risk category is disclosed. The proposed systems and methods describe an autonomous digital assistant that accepts contract-related queries from end-users and generates conversational responses. For example, an end-user may request information about the contract's terms, and the system can automatically provide intelligent analytical responses that bypass the need to manually evaluate the various features of the document.

Abstract: An intelligent table-based question answering system receives an input user query requesting information from an input table to generate a response to the input query. Two types of user queries including textual user queries and scalar user queries that require at least one mathematical operation to be executed can be processed for answer generation. The input table is processed to generate a paragraph of table text which includes row and column information extracted from the input table. The input query is provided along with the paragraph of table text to a deep Quans model which outputs a candidate answer that forms a textual portion of the response. Also, the candidate answer is reverse mapped to the input table and a portion of the input table including the candidate answer is provided as a tabular portion of the response.

Abstract: In some examples, artificial intelligence and machine learning based conversational agent may include ascertaining, based on a chat conducted with a conversational chatbot, a request by a user to purchase a product, and an attribute associated with the user. An intent associated with the user to purchase the product may be generated. Further, a catalog that includes a plurality of products that match the request by the user to purchase the product may be generated. Selection of a product from the plurality of products may be received. Identification of a quantity associated with the selected product may be received. A purchase request may be generated. A purchase order associated with the selected product may be generated. Further, based on the purchase order associated with the selected product, the selected product may be procured for the user.

Abstract: An automatic question answering system serves to respond to questions pertaining to a user-selected domain of a plurality of domains. A plurality of documents pertaining to the domain are received and processed to identify candidate answers. Questions corresponding to the candidate answers are automatically generated. The candidate answers and the questions are used to train a machine comprehension (MC) model. A user interface allows the selection of one of the plurality of domains for querying by a user. When a user query pertaining to the selected domain is received, the user query is analyzed to identify a relevant context from the plurality of documents. An answer span to the user query identified by the MC model from the relevant context is used to frame a complete response to the user query.

Abstract: Examples of a textual entailment generation system are provided. The system obtains a query from a user and implements an artificial intelligence component to identify a premise, a word index, and a premise index associated with the query. The system may implement a first cognitive learning operation to determine a plurality of hypothesis and a hypothesis index corresponding to the premise. The system may generate a confidence index for each of the plurality of hypothesis based on a comparison of the hypothesis index with the premise index. The system may determine an entailment value, a contradiction value, and a neutral entailment value based on the confidence index for each of the plurality of hypothesis. The system may generate an entailment result relevant for resolving the query comprising the plurality of hypothesis along with the corresponding entailed output index.

Abstract: In some examples, artificial intelligence and machine learning based conversational agent may include ascertaining, based on a chat conducted with a conversational chatbot, a request by a user to purchase a product, and an attribute associated with the user. An intent associated with the user to purchase the product may be generated. Further, a catalog that includes a plurality of products that match the request by the user to purchase the product may be generated. Selection of a product from the plurality of products may be received. Identification of a quantity associated with the selected product may be received. A purchase request may be generated. A purchase order associated with the selected product may be generated. Further, based on the purchase order associated with the selected product, the selected product may be procured for the user.