Abstract:
A sequence conversion method includes receiving a source sequence, converting the source sequence into a source vector representation sequence, obtaining at least two candidate target sequences and a translation probability value of each of the at least two candidate target sequences according to the source vector representation sequence, adjusting the translation probability value of each candidate target sequence, selecting an output target sequence from the at least two candidate target sequences according to an adjusted translation probability value of each candidate target sequence, and outputting the output target sequence. Hence, loyalty of a target sequence to a source sequence can be improved during sequence conversion.
Abstract:
The present application relates to natural language processing and discloses a sequence conversion method. The method includes: obtaining a source sequence from an input signal; converting the source sequence into one or more source context vectors; obtaining a target context vector corresponding to each source context vector; combining the target context vectors to obtain the target sequence; and outputting the target sequence. A weight vector is applied on a source context vector and a reference context vector, to obtain a target context vector. The source sequence and the target sequence are representations of natural language contents. The claimed process improves faithfulness of converting the source sequence to the target sequence.
Abstract:
The present disclosure discloses a terminal, a touch control unit, a touchscreen, a screen protector, and an operation detection apparatus and method, and pertains to the field of terminal control. The terminal includes a touchscreen, a controller connected to the touchscreen, and at least one touch control unit, where the touch control unit includes a detection electrode, an induction electrode, and a conducting wire connecting the detection electrode and the induction electrode. The detection electrode is located outside a touch area of the touchscreen, the induction electrode is located in the touch area of the touchscreen, and the induction electrode is coupled to at least one capacitance node on the touchscreen. An existing capacitance node on the touchscreen identifies a touch operation performed by a user on the detection electrode disposed outside the touchscreen, and the terminal is controlled according to the touch operation.
Abstract:
A method and an apparatus for recommending a message. The method for recommending a message in the present disclosure includes separately parsing a first message published by a first user on a network and a second message published by a second user on the network, obtaining interest description information of the first message and topic description information of the second message, where the second user is another user except the first user, comparing the topic description information with the interest description information, and calculating a similarity of the topic description information and the interest description information; and if the similarity is greater than or equal to a predetermined value, pushing the second message published by the second user to the first user. A user can conveniently and flexibly obtain a message in which the user is interested in the embodiments of the present disclosure.
Abstract:
A help processing method and device based on semantic recognition are presented. The method includes receiving, by user equipment, a search request entered by a user, where the search request includes information about a problem statement described in a natural language; performing semantic recognition processing on the information about the problem statement, to obtain information about a search intention of the user; and searching a database using the information about the search intention as a search term, to obtain help content needed by the user.
Abstract:
One example method includes obtaining L1 first decoding paths of an (i−1)th group of to-be-decoded bits, where i is an integer, received data corresponds to P groups of to-be-decoded bits, and 1
Abstract:
A learning-to-rank method based on reinforcement learning, including obtaining, by a server, a historical search word, and obtaining M documents corresponding to the historical search word; ranking, by the server, the M documents to obtain a target document ranking list; obtaining, by the server, a ranking effect evaluation value of the target document ranking list; using, by the server, the historical search word, the M documents, the target document ranking list, and the ranking effect evaluation value as a training sample, and adding the training sample into a training sample set.
Abstract:
An apparatus is pre-equipped with a plurality of dialogue robots, and each dialogue robot is configured to conduct a human-computer dialogue based on at least one topic. The method includes: obtaining a text entered by a user; determining at least one topic related to the text, and determining a target dialogue robot from the plurality of dialogue robots based on the at least one topic related to the text and a predefined mapping relationship between a dialogue robot and a topic, where a target topic corresponding to the target dialogue robot is some or all of the at least one topic related to the text; and allocating the text to the target dialogue robot and obtaining a reply for the text from the target dialogue robot, where the reply is generated by the target dialogue robot based on at least one semantic understanding of the text.
Abstract:
A method and an apparatus for determining a semantic matching degree, where the method includes acquiring a first sentence and a second sentence, dividing the first sentence and the second sentence into x and y sentence fragments, respectively, performing a convolution operation on word vectors in each sentence fragment of the first sentence and word vectors in each sentence fragment of the second sentence to obtain a three-dimensional tensor, performing integration or screening on adjacent vectors in the one-dimensional vectors of x rows and y columns, until the three-dimensional tensor is combined into a one-dimensional target vector, and determining a semantic matching degree between the first sentence and the second sentence according to the target vector.
Abstract:
A sequence conversion method includes receiving a source sequence, converting the source sequence into a source vector representation sequence, obtaining at least two candidate target sequences and a translation probability value of each of the at least two candidate target sequences according to the source vector representation sequence, adjusting the translation probability value of each candidate target sequence, selecting an output target sequence from the at least two candidate target sequences according to an adjusted translation probability value of each candidate target sequence, and outputting the output target sequence. Hence, loyalty of a target sequence to a source sequence can be improved during sequence conversion.