Abstract: A method for speech recognition using cadence patterns is provided. The method includes identifying speech cadence in user speech, which includes a plurality of sounds and pauses. At least one speech cadence pattern is identified from the plurality of sounds and pauses. The user speech is transcribed, and the transcribed user speech is modified based on the identified speech cadence pattern.

Abstract: Embodiments for managing chatbots are provided. Chatbot interaction information is received. At least one chatbot interaction within the received chatbot interaction information is identified. The at least one identified chatbot interaction includes an initial utterance provided by a user, an unsuccessful response generated responsive to the initial utterance, a subsequent utterance provided by the user, and a successful response generated responsive to the subsequent utterance. The successful response is associated with a chatbot intent. A confidence score for associating the initial utterance with utilization of the chatbot intent by a chatbot is calculated.

Abstract: A method, system, and apparatus are provided for recommending machine learning (ML) project resources for completing a user project by generating indexed project metadata for a plurality of ML projects, generating search metadata for a search request for ML project resources to develop an ML project, and then evaluating the search metadata against the indexed project metadata for each ML project to form a relevancy assessment which is used to order trained models from the ML projects and to display one or more recommended ML project resources comprising one or more of the plurality of trained models having a relevancy assessment exceeding a relevancy threshold.

Abstract: An embodiment of the invention may include a method, computer program product, and system for generating ground truth data for a plurality of cognitive capabilities within an overall cognitive system. The embodiment may include configuring multiple sets of training data. Each set of training data corresponds to a separate cognitive capability. The embodiment may include displaying a set of ground truth curation activities via a user interface. The embodiment may include determining the ground truth curation activities performed for a first type of data for a first duration. The first type of data is selected from the single set of grouped training data. The embodiment may include determining whether the first duration has exceeded a pre-determined threshold. The embodiment may include switching the curation activities to a second type of data. The second type of data is selected from the single set of grouped data.

Abstract: Embodiments for managing chatbots are provided. Chatbot interaction information is received. At least one chatbot interaction within the received chatbot interaction information is identified. The at least one identified chatbot interaction includes an initial utterance provided by a user, an unsuccessful response generated responsive to the initial utterance, a subsequent utterance provided by the user, and a successful response generated responsive to the subsequent utterance. The successful response is associated with a chatbot intent. A confidence score for associating the initial utterance with utilization of the chatbot intent by a chatbot is calculated.

Abstract: A method improves a functionality of a conversational agent that is generated by an artificial intelligence (AI) system. A conversational agent receives a first utterance from a first entity. The AI system identifies an unverified response to the first utterance; sends the unverified response to the first entity; and receives a positive feedback indication about the unverified response from the first entity. The AI system searches a data store in order to identify an entry for a second utterance by a second entity, where the second entity has sent a positive feedback for the unverified response. The AI system sends the second utterance and the unverified response to the first entity, and receives a positive feedback for the unverified response to the second utterance from the first entity in order to mark the unverified response as a verified response, which responds to future receipts of the first utterance.

Abstract: A method, computer system, and computer program product for chat protection. The embodiment may include receiving, in a chat session, a user-spoken communication and one or more inputs specific to a software program. The embodiment may also include predicting a next communication by the user based on the received user spoken communication and the one or more inputs. The embodiment may further include, in response to the predicted next communication containing untoward content, performing a remedial action.

Abstract: A method, computer system, and computer program product for chat protection. The embodiment may include receiving, in a chat session, a user-spoken communication and one or more inputs specific to a software program. The embodiment may also include predicting a next communication by the user based on the received user spoken communication and the one or more inputs. The embodiment may further include, in response to the predicted next communication containing untoward content, performing a remedial action.

Abstract: A method, system, and apparatus are provided for recommending machine learning (ML) project resources for completing a user project by generating indexed project metadata for a plurality of ML projects, generating search metadata for a search request for ML project resources to develop an ML project, and then evaluating the search metadata against the indexed project metadata for each ML project to form a relevancy assessment which is used to order trained models from the ML projects and to display one or more recommended ML project resources comprising one or more of the plurality of trained models having a relevancy assessment exceeding a relevancy threshold.

Abstract: A method improves a functionality of a conversational agent that is generated by an artificial intelligence (AI) system. A conversational agent receives a first utterance from a first entity. The AI system identifies an unverified response to the first utterance; sends the unverified response to the first entity; and receives a positive feedback indication about the unverified response from the first entity. The AI system searches a data store in order to identify an entry for a second utterance by a second entity, where the second entity has sent a positive feedback for the unverified response. The AI system sends the second utterance and the unverified response to the first entity, and receives a positive feedback for the unverified response to the second utterance from the first entity in order to mark the unverified response as a verified response, which responds to future receipts of the first utterance.

Abstract: An embodiment of the invention may include a method, computer program product, and system for generating ground truth data for a plurality of cognitive capabilities within an overall cognitive system. The embodiment may include configuring multiple sets of training data. Each set of training data corresponds to a separate cognitive capability. The embodiment may include displaying a set of ground truth curation activities via a user interface. The embodiment may include determining the ground truth curation activities performed for a first type of data for a first duration. The first type of data is selected from the single set of grouped training data. The embodiment may include determining whether the first duration has exceeded a pre-determined threshold. The embodiment may include switching the curation activities to a second type of data. The second type of data is selected from the single set of grouped data.

Abstract: An embodiment of the invention may include a method, computer program product, and system for generating ground truth data for a plurality of cognitive capabilities within an overall cognitive system. The embodiment may include configuring multiple sets of training data. Each set of training data corresponds to a separate cognitive capability. The embodiment may include displaying a set of ground truth curation activities via a user interface. The embodiment may include determining the ground truth curation activities performed for a first type of data for a first duration. The first type of data is selected from the single set of grouped training data. The embodiment may include determining whether the first duration has exceeded a pre-determined threshold. The embodiment may include switching the curation activities to a second type of data. The second type of data is selected from the single set of grouped data.

Abstract: An embodiment of the invention may include a method, computer program product, and system for generating ground truth data for a plurality of cognitive capabilities within an overall cognitive system. The embodiment may include configuring multiple sets of training data. Each set of training data corresponds to a separate cognitive capability. The embodiment may include displaying a set of ground truth curation activities via a user interface. The embodiment may include determining the ground truth curation activities performed for a first type of data for a first duration. The first type of data is selected from the single set of grouped training data. The embodiment may include determining whether the first duration has exceeded a pre-determined threshold. The embodiment may include switching the curation activities to a second type of data. The second type of data is selected from the single set of grouped data.

Abstract: Curating high-quality ground truth is an important but difficult part of training a cognitive system. The invention greatly simplifies this process by determining the value that particular training data has in improving existing ground truth. Candidate training data of different types (text, audio, images) is extracted from an interaction log, and each entry is analyzed to arrive at a training value score. The analysis generates multiple component scores which are combined for the final score. The component scores may include a per-feature variability score, a cross-feature variability score, and an accuracy score. A set of the unverified entries may be presented to a user based on the training value scores, and the user can select which of the entries in the set should be included as new ground truths. The ground truths can then be updated by adding the selected entries.

Abstract: Curating high-quality ground truth is an important but difficult part of training a cognitive system. The invention greatly simplifies this process by determining the value that particular training data has in improving existing ground truth. Candidate training data of different types (text, audio, images) is extracted from an interaction log, and each entry is analyzed to arrive at a training value score. The analysis generates multiple component scores which are combined for the final score. The component scores may include a per-feature variability score, a cross-feature variability score, and an accuracy score. A set of the unverified entries may be presented to a user based on the training value scores, and the user can select which of the entries in the set should be included as new ground truths. The ground truths can then be updated by adding the selected entries.

Abstract: An approach is provided to discover new portmanteau, such as when ingesting documents into a question answering (QA) system. The approach works by analyzing a words included in electronic documents and identifies words as being possible portmanteaus. To analyze a portmanteau found in a document, the approach identifies morphemes that are included in the identified portmanteau and candidate words that correspond to each of the identified morphemes. A meaning for the new portmanteau is then derived from the meanings of the candidate word meanings.

Abstract: An approach is provided to discover new portmanteau, such as when ingesting documents into a question answering (QA) system. The approach works by analyzing a words included in electronic documents and identifies words as being possible portmanteaus. To analyze a portmanteau found in a document, the approach identifies morphemes that are included in the identified portmanteau and candidate words that correspond to each of the identified morphemes. A meaning for the new portmanteau is then derived from the meanings of the candidate word meanings.

Abstract: An approach is provided to discover new portmanteau, such as when ingesting documents into a question answering (QA) system. The approach works by analyzing a words included in electronic documents and identifies words as being possible portmanteaus. To analyze a portmanteau found in a document, the approach identifies morphemes that are included in the identified portmanteau and candidate words that correspond to each of the identified morphemes. A meaning for the new portmanteau is then derived from the meanings of the candidate word meanings.

Abstract: An approach is provided to discover new portmanteau, such as when ingesting documents into a question answering (QA) system. The approach works by analyzing a words included in electronic documents and identifies words as being possible portmanteaus. To analyze a portmanteau found in a document, the approach identifies morphemes that are included in the identified portmanteau and candidate words that correspond to each of the identified morphemes. A meaning for the new portmanteau is then derived from the meanings of the candidate word meanings.

Abstract: An approach is provided to discover new portmanteau, such as when ingesting documents into a question answering (QA) system. The approach works by analyzing a words included in electronic documents and identifies words as being possible portmanteaus. To analyze a portmanteau found in a document, the approach identifies morphemes that are included in the identified portmanteau and candidate words that correspond to each of the identified morphemes. A meaning for the new portmanteau is then derived from the meanings of the candidate word meanings.