Abstract: Methods, programming, and system for modifying a slot value are described herein. In a non-limiting embodiment, an intent may be determined based on a first utterance. A first slot-value pair may be obtained for the first utterance based on the intent, the first slot-value pair including a first slot and a first value associated with the first slot. A second value associated with the first slot may be identified, the second value being identified from a second utterance that was previously received. Based on the intent and the first slot, a type of update to be performed with respect to the second value may be determined. The second value may then be updated based on the first value and the type of update.

Abstract: Methods, programming, and system for identifying one or more variable slots within an utterance are described herein. In a non-limiting embodiment, a first slot-value pair for an utterance may be obtained. The first slot-value pair may include a first slot and a first value associated with the slot. The first slot may be of a first entity type, where an intent and a data object are estimated based on the first utterance. A data structure representing the data object may be identified. Based on the intent, a first variable slot in the data structure associated with the first entity type may be determined, where the first variable slot may be associated with at least one of: multiple values and an adjustable value. Based on the intent, the first value may be assigned to the first variable slot in the data structure.

Abstract: Methods, programming, and system for modifying a slot value are described herein. In a non-limiting embodiment, an intent may be determined based on a first utterance. A first slot-value pair may be obtained for the first utterance based on the intent, the first slot-value pair including a first slot and a first value associated with the first slot. A second value associated with the first slot may be identified, the second value being identified from a second utterance that was previously received. Based on the intent and the first slot, a type of update to be performed with respect to the second value may be determined. The second value may then be updated based on the first value and the type of update.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The disclosure provides a computerized framework for automatically generating chatbot responses to produce domain-specific responses that mimic native styles unique to particular domains. The disclosed systems and methods construct domain-specific word-graphs based on account activity from specific domains and generate word-patterns. New words obtained from the patterns in the graph are introduced to transform the regular response. The graph is then pruned using data-driven thresholds in order to avoid spurious transformations, and paragraph vectors are also utilized to assign relevance scores to generated patterns such that only the patterns that are contextually similar to the original response (generic/regular response) are used.

Abstract: Methods, programming, and system for modifying a slot value are described herein. In a non-limiting embodiment, an intent may be determined based on a first utterance. A first slot-value pair may be obtained for the first utterance based on the intent, the first slot-value pair including a first slot and a first value associated with the first slot. A second value associated with the first slot may be identified, the second value being identified from a second utterance that was previously received. Based on the intent and the first slot, a type of update to be performed with respect to the second value may be determined. The second value may then be updated based on the first value and the type of update.

Abstract: Methods, programming, and system for modifying a slot value are described herein. In a non-limiting embodiment, an intent may be determined based on a first utterance. A first slot-value pair may be obtained for the first utterance based on the intent, the first slot-value pair including a first slot and a first value associated with the first slot. A second value associated with the first slot may be identified, the second value being identified from a second utterance that was previously received. Based on the intent and the first slot, a type of update to be performed with respect to the second value may be determined. The second value may then be updated based on the first value and the type of update.

Abstract: Methods, programming, and system for identifying one or more variable slots within an utterance are described herein. In a non-limiting embodiment, a first slot-value pair for an utterance may be obtained. The first slot-value pair may include a first slot and a first value associated with the slot. The first slot may be of a first entity type, where an intent and a data object are estimated based on the first utterance. A data structure representing the data object may be identified. Based on the intent, a first variable slot in the data structure associated with the first entity type may be determined, where the first variable slot may be associated with at least one of: multiple values and an adjustable value. Based on the intent, the first value may be assigned to the first variable slot in the data structure.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The disclosure provides a computerized framework for automatically generating chatbot responses to produce domain-specific responses that mimic native styles unique to particular domains. The disclosed systems and methods construct domain-specific word-graphs based on account activity from specific domains and generate word-patterns. New words obtained from the patterns in the graph are introduced to transform the regular response. The graph is then pruned using data-driven thresholds in order to avoid spurious transformations, and paragraph vectors are also utilized to assign relevance scores to generated patterns such that only the patterns that are contextually similar to the original response (generic/regular response) are used.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The disclosure provides a computerized framework for automatically generating chatbot responses to produce domain-specific responses that mimic native styles unique to particular domains. The disclosed systems and methods construct domain-specific word-graphs based on account activity from specific domains and generate word-patterns. New words obtained from the patterns in the graph are introduced to transform the regular response. The graph is then pruned using data-driven thresholds in order to avoid spurious transformations, and paragraph vectors are also utilized to assign relevance scores to generated patterns such that only the patterns that are contextually similar to the original response (generic/regular response) are used.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The disclosure provides a computerized framework for automatically generating chatbot responses to produce domain-specific responses that mimic native styles unique to particular domains. The disclosed systems and methods construct domain-specific word-graphs based on account activity from specific domains and generate word-patterns. New words obtained from the patterns in the graph are introduced to transform the regular response. The graph is then pruned using data-driven thresholds in order to avoid spurious transformations, and paragraph vectors are also utilized to assign relevance scores to generated patterns such that only the patterns that are contextually similar to the original response (generic/regular response) are used.

Abstract: Methods and arrangements for handling queries for a discussion thread. A contemplated method includes: receiving a query; automatically classifying the query as subjective, objective or neither; and upon classifying the query as subjective or objective: calculating, for discussion threads of the query, at least one of: a subjectivity score and an objectivity score; determining a degree of relevance to the query of at least one of: the discussion threads, and at least one post in the at least one discussion thread; and ranking the discussion threads based on said calculating and determining. Other variants and embodiments are broadly contemplated herein.

Abstract: Methods and arrangements for handling queries for a discussion thread. A contemplated method includes: receiving a query; automatically classifying the query as subjective, objective or neither; and upon classifying the query as subjective or objective: calculating, for discussion threads of the query, at least one of: a subjectivity score and an objectivity score; determining a degree of relevance to the query of at least one of: the discussion threads, and at least one post in the at least one discussion thread; and ranking the discussion threads based on said calculating and determining. Other variants and embodiments are broadly contemplated herein.