Abstract: Various technologies relating to constructing an answer to a query are described herein, wherein the answer is in list form. The answer includes a header and a list element. A deep model receives content of a webpage that is deemed relevant to the query by a search engine and constructs the answer to the webpage upon receipt of the query.

Abstract: Various technologies relating to constructing an answer to a query are described herein, wherein the answer is in list form. The answer includes a header and a list element. A deep model receives content of a webpage that is deemed relevant to the query by a search engine and constructs the answer to the webpage upon receipt of the query.

Abstract: Various technologies relating to constructing an answer to a query are described herein, wherein the answer is in list form. The answer includes a header and a list element. A deep model receives content of a webpage that is deemed relevant to the query by a search engine and constructs the answer to the webpage upon receipt of the query.

Abstract: Various technologies relating to constructing an answer to a query are described herein, wherein the answer is in list form. The answer includes a header and a list element. A deep model receives content of a webpage that is deemed relevant to the query by a search engine and constructs the answer to the webpage upon receipt of the query.

Abstract: Various technologies relating to constructing an answer to a query are described herein, wherein the answer is in list form. The answer includes a header and a list element. A deep model receives content of a webpage that is deemed relevant to the query by a search engine and constructs the answer to the webpage upon receipt of the query.

Abstract: Representative embodiments disclose mechanisms to provide direct answers to a query submitted by a user. The mechanisms are tailored so that the answers presented have a high confidence of being correct. A plurality of document segments that are relevant to the query are selected. The selected segments are submitted to a trained machine reading comprehension model along with the query. The result is an extracted answer for one or more of the submitted segments. A subset of the extracted answers are clustered and an answer for each cluster having at least a threshold number of answers are selected as direct answers. The direct answers are presented in a format suitable to the number of selected direct answers.

Abstract: Various technologies relating to constructing an answer to a query are described herein, wherein the answer is in list form. The answer includes a header and a list element. A deep model receives content of a webpage that is deemed relevant to the query by a search engine and constructs the answer to the webpage upon receipt of the query.

Abstract: Examples of the present disclosure describe systems and methods relating to generating a relevance score on a given natural language answer to a natural language query for ranking the answer among other answers for the query, while generating a summary passage and a likely query to the given passage. For instance, multi-layered, recurrent neural networks may be used to encode the query and the passage, along with a multi-layered neural network for information retrieval features, to generate a relevant score for the passage. A multi-layered, recurrent neural network with soft attention and sequence-to-sequence learning task may be used as a decoder to generate a summary passage. A common encoding neural network may be employed to encode the passage for the ranking and the summarizing, in order to present concise and accurate natural language answers to the query.

Abstract: Representative embodiments disclose mechanisms to provide direct answers to a query submitted by a user. The mechanisms are tailored so that the answers presented have a high confidence of being correct. A plurality of document segments that are relevant to the query are selected. The selected segments are submitted to a trained machine reading comprehension model along with the query. The result is an extracted answer for one or more of the submitted segments. A subset of the extracted answers are clustered and an answer for each cluster having at least a threshold number of answers are selected as direct answers. The direct answers are presented in a format suitable to the number of selected direct answers.

Abstract: Examples of the present disclosure describe systems and methods relating to generating a relevance score on a given natural language answer to a natural language query for ranking the answer among other answers for the query, while generating a summary passage and a likely query to the given passage. For instance, multi-layered, recurrent neural networks may be used to encode the query and the passage, along with a multi-layered neural network for information retrieval features, to generate a relevant score for the passage. A multi-layered, recurrent neural network with soft attention and sequence-to-sequence learning task may be used as a decoder to generate a summary passage. A common encoding neural network may be employed to encode the passage for the ranking and the summarizing, in order to present concise and accurate natural language answers to the query.

Abstract: Examples of the present disclosure describe systems and methods relating to generating a relevance score on a given natural language answer to a natural language query for ranking the answer among other answers for the query, while generating a summary passage and a likely query to the given passage. For instance, multi-layered, recurrent neural networks may be used to encode the query and the passage, along with a multi-layered neural network for information retrieval features, to generate a relevant score for the passage. A multi-layered, recurrent neural network with soft attention and sequence-to-sequence learning task may be used as a decoder to generate a summary passage. A common encoding neural network may be employed to encode the passage for the ranking and the summarizing, in order to present concise and accurate natural language answers to the query.

Abstract: Examples of the present disclosure describe systems and methods relating to generating a relevance score on a given natural language answer to a natural language query for ranking the answer among other answers for the query, while generating a summary passage and a likely query to the given passage. For instance, multi-layered, recurrent neural networks may be used to encode the query and the passage, along with a multi-layered neural network for information retrieval features, to generate a relevant score for the passage. A multi-layered, recurrent neural network with soft attention and sequence-to-sequence learning task may be used as a decoder to generate a summary passage. A common encoding neural network may be employed to encode the passage for the ranking and the summarizing, in order to present concise and accurate natural language answers to the query.

Abstract: Aspects of the technology described herein increase the efficiency of a search session by determining whether fresh content is likely to be responsive to the user's query. Whether fresh content is likely to be responsive to a specific query is determined by retrieving social media posts that are responsive to the query. The social media posts are evaluated for virality, which is the tendency of a social media post to be circulated rapidly and widely from one Internet user to another. The virality of a social media post can be determined by comparing a number of times the social media post has been re-communicated by individual users. Queries that return viral social media posts may be classified as seeking fresh content.

Abstract: A natural language query may be transformed to a transformed natural language while keeping sufficient semantic meaning such that the query may be transformed. A natural language query may be received by a computing device and sent to a natural language transformation model for transformation. The transformation may use a variety of techniques including stop word removal, stop structure removal, noun phrase/entity detection, key concept detection, dependency filtering. The techniques may be sequenced.

Abstract: Aspects of the technology described herein increase the efficiency of a search session by determining whether fresh content is likely to be responsive to the user's query. Whether fresh content is likely to be responsive to a specific query is determined by retrieving social media posts that are responsive to the query. The social media posts are evaluated for virality, which is the tendency of a social media post to be circulated rapidly and widely from one Internet user to another. The virality of a social media post can be determined by comparing a number of times the social media post has been re-communicated by individual users. Queries that return viral social media posts may be classified as seeking fresh content.

Abstract: The present disclosure generally relates to ad auction optimization. In some examples, methods, systems, and computer programs for ad auction optimization using machine learning algorithms to estimate a likelihood that a consumer will purchase an advertised product and balance long term and short term goals to determine modeled data for a keyword in an auction are described.