Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for resolving temporal ambiguities are presented. A natural language input may be received. A temporal component of the input may be identified. A determination may be made that the temporal component includes a conjunction that separates temporal meeting block alternatives. A temporal ambiguity may be identified in one of the meeting block alternatives. A plurality of syntax tree permutations may be generated for the meeting block alternative where the ambiguity was identified. A machine learning model that has been trained to identify a most relevant permutation for a given natural language input may be applied to each of the plurality of permutations. A temporal meeting block alternative corresponding to the most relevant permutation may be surfaced.

Abstract: The disclosure relates to systems and methods for identifying entities related to a task in a natural language input. An entity detection model is provided which receives a natural language input. The entity detection model processes the natural language input using an entity encoder and an input encoder. The entity encoder identifies and encodes relevant entities while the input encoder generates a contextual encoding which represents contextual information associated with a relevant entity. The encoded entity and contextual encodings may then be combined and processed to generate a probability score for an identified entity. A negation constraint model is also disclosed. The negation constraint model receives the natural language input and the identified entities. The natural language input is analyzed to identify negation cues and determine if the negation cue is associated with an identified entity.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for resolving temporal ambiguities are presented. A natural language input may be received. A temporal component of the input may be identified. A determination may be made that the temporal component includes a conjunction that separates temporal meeting block alternatives. A temporal ambiguity may be identified in one of the meeting block alternatives. A plurality of syntax tree permutations may be generated for the meeting block alternative where the ambiguity was identified. A machine learning model that has been trained to identify a most relevant permutation for a given natural language input may be applied to each of the plurality of permutations. A temporal meeting block alternative corresponding to the most relevant permutation may be surfaced.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for identifying relevant content in a natural language input are presented. An email may be received. A machine learning model may be applied to the email. The machine learning model may have been trained to rank sentences based on their relevance to a schedule meeting task. The machine learning model may comprise: an embedding layer for generating an embedding for each word in the email; a distinct sentence aggregation layer for aggregating the embeddings for each word in the email into a distinct embedding for each of the sentences in the email; a contextual aggregation layer for aggregating each distinct embedding for each of the sentences into a contextual embedding for each of the sentences; and a scoring layer for scoring and ranking each of the sentences based on their relevance to the schedule meeting task.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for resolving temporal ambiguities are presented. A natural language input may be received. A temporal component of the input may be identified. A determination may be made that the temporal component includes a conjunction that separates temporal meeting block alternatives. A temporal ambiguity may be identified in one of the meeting block alternatives. A plurality of syntax tree permutations may be generated for the meeting block alternative where the ambiguity was identified. A machine learning model that has been trained to identify a most relevant permutation for a given natural language input may be applied to each of the plurality of permutations. A temporal meeting block alternative corresponding to the most relevant permutation may be surfaced.

Abstract: The disclosure relates to systems and methods for identifying entities related to a task in a natural language input. An entity detection model is provided which receives a natural language input. The entity detection model processes the natural language input using an entity encoder and an input encoder. The entity encoder identifies and encodes relevant entities while the input encoder generates a contextual encoding which represents contextual information associated with a relevant entity. The encoded entity and contextual encodings may then be combined and processed to generate a probability score for an identified entity. A negation constraint model is also disclosed. The negation constraint model receives the natural language input and the identified entities. The natural language input is analyzed to identify negation cues and determine if the negation cue is associated with an identified entity.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for assisting with scheduling a meeting are presented. A message comprising a plurality of sentences may be received. A hierarchical attention model may be utilized to identify a subset of sentences of the plurality of sentences that are relevant to a scheduling of the meeting. A subset of words in the subset of sentences that are potentially relevant to scheduling of the meeting may be identified based on relating to at least one meeting parameter. The subset of words may be split into a first group comprising words from the subset of words that are above a meeting relevance threshold value, and a second group comprising words from the subset of words that are below a meeting relevance threshold value. An automated action associated with scheduling the meeting may be caused to be performed.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for identifying relevant content in a natural language input are presented. An email may be received. A machine learning model may be applied to the email. The machine learning model may have been trained to rank sentences based on their relevance to a schedule meeting task. The machine learning model may comprise: an embedding layer for generating an embedding for each word in the email; a distinct sentence aggregation layer for aggregating the embeddings for each word in the email into a distinct embedding for each of the sentences in the email; a contextual aggregation layer for aggregating each distinct embedding for each of the sentences into a contextual embedding for each of the sentences; and a scoring layer for scoring and ranking each of the sentences based on their relevance to the schedule meeting task.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for resolving temporal ambiguities are presented. A natural language input may be received. A temporal component of the input may be identified. A determination may be made that the temporal component includes a conjunction that separates temporal meeting block alternatives. A temporal ambiguity may be identified in one of the meeting block alternatives. A plurality of syntax tree permutations may be generated for the meeting block alternative where the ambiguity was identified. A machine learning model that has been trained to identify a most relevant permutation for a given natural language input may be applied to each of the plurality of permutations. A temporal meeting block alternative corresponding to the most relevant permutation may be surfaced.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for assisting with scheduling a meeting are presented. A message comprising a plurality of sentences may be received. A hierarchical attention model may be utilized to identify a subset of sentences of the plurality of sentences that are relevant to a scheduling of the meeting. A subset of words in the subset of sentences that are potentially relevant to scheduling of the meeting may be identified based on relating to at least one meeting parameter. The subset of words may be split into a first group comprising words from the subset of words that are above a meeting relevance threshold value, and a second group comprising words from the subset of words that are below a meeting relevance threshold value. An automated action associated with scheduling the meeting may he caused to be performed.