Abstract: The present disclosure relates to a device and a system for testing flatness. The device for testing flatness includes a base, a testing platform, and a ranging sensor. The testing platform is assembled on the base. The testing platform includes a supporting structure. The supporting structure is disposed on the side of the testing platform away from the base and is used to support a to-be-tested board. The structure matches the structure of the to-be-tested board. The ranging sensor is disposed on the side of the testing platform away from the base. After the to-be-tested board is placed on the testing platform, the ranging sensor is used to test distances between a number N of to-be-tested positions on the to-be-tested board and the ranging sensor, to obtain N pieces of distance information, and the N pieces of distance information are used to determine the flatness of the to-be-tested board, where N is an integer greater than 2.

Abstract: A folding device is provided. The folding device includes a bearing and fixing mechanism (1) configured to bear and fix a main body portion (21) of a to-be-folded device (2); a folding mechanism (3) located on at least one side of the bearing and fixing mechanism (1), the folding mechanism (3) being rotatably connected to the bearing and fixing mechanism (1) and configured to bear and fix a to-be-folded portion (22) of the to-be-folded device (2); and a driving mechanism (4) connected to the folding mechanism (3), the driving mechanism (4) being configured to drive the folding mechanism (3) to turn relative to the bearing and fixing mechanism (1), so as to fold the to-be-folded portion (22) to a side in a thickness direction of the main body portion (21). The folding device can fold automatically, and manual folding is avoided.

Abstract: Provided are a data processing method for a dialogue system, an apparatus, a device and a medium. The method includes: obtaining a pre-configured task description, wherein the task description comprises at least one task name and at least one task attribute corresponding to a respective task name; extracting, based on a reading comprehension technique, an answer corresponding to the task description from content of a current dialogue with a user; and completing the dialogue with the user according to the answer and a pre-generated dialogue flow.

Abstract: A model training method, a method and an apparatus for human-machine interaction. The method includes: acquiring a sample set corresponding to a template; constructing positive example pairs and negative example pairs for a contrastive learning task based on the sample set; performing contrastive learning training on a pre-training model based on the positive example pairs and the negative example pairs for the contrastive learning task.

Abstract: A semantic understanding method, includes: acquiring a query statement and a preceding dialogue; rewriting the query statement based on a preset rule to generate a target query statement if it is recognized that the query statement meets a rule rewriting condition according to the query statement and the preceding dialogue; rewriting the query statement based on a rewriting model to generate the target query statement if it is recognized that the query statement does not meet the rule rewriting condition according to the query statement and the preceding dialogue; and performing intention recognition according to the target query statement to generate an intention recognition result.

Abstract: A method for processing a question is performed by an electronic device. The method includes: receiving a question to be processed from a user input; determining a first similarity between the question to be processed and each candidate question in at least one reference question-answer (Q&A) pair; determining a second similarity between the question to be processed and the at least one reference Q&A pair based on the first similarity; determining a target Q&A pair from the at least one reference Q&A pair based on the second similarity; and replying to the user for the question to be processed based on a target answer in the target Q&A pair.

Abstract: The present disclosure provides intent recognition model training and intent recognition methods and apparatuses, and relates to the field of artificial intelligence technologies. The intent recognition model training method includes: acquiring training data including a plurality of training texts and first annotation intents of the plurality of training texts; constructing a neural network model including a feature extraction layer and a first recognition layer; and training the neural network model according to word segmentation results of the plurality of training texts and the first annotation intents of the plurality of training texts to obtain an intent recognition model. The method for intent recognition includes: acquiring a to-be-recognized text; and inputting word segmentation results of the to-be-recognized text to an intent recognition model, and obtaining a first intent result and a second intent result of the to-be-recognized text according to an output result of the intent recognition model.

Abstract: Embodiments of the present disclosure provide a method and apparatus for outputting analysis abnormality information in a spoken language understanding. The method includes: acquiring first spoken language information and a first analysis result for the first spoken language information; determining whether the first analysis result includes intent information; determining whether the first spoken language information includes a predefined word slot value, in response to determining the first analysis result not including the intent information; and outputting information indicating an existence of an abnormality in the first analysis result, in response to determining the first spoken language information including the predefined word slot value.

Abstract: A method for generating a dialogue state includes: acquiring a target dialogue state of a previous round of dialogue and dialogue information of a current round of dialogue; generating an initial dialogue state of the current round of dialogue according to the target dialogue state of the previous round of dialogue and the dialogue information of the current round of dialogue; and generating a target dialogue state of the current round of dialogue according to the initial dialogue state of the current round of dialogue and the dialogue information of the current round of dialogue.

Abstract: The disclosure provides a method for generating a dialogue. The method includes: obtaining an input sentence; determining a type of a task-based response sentence that is to be generated, by updating a current dialogue state based on the input sentence; generating the task-based response sentence by inputting the input sentence into a task-based dialogue response generator; and determining the task-based response sentence as a target response sentence in response to the type of the task-based response sentence being a designated type.

Abstract: The present disclosure provides a method and apparatus for update processing of a question answering system, relates to the technical field of artificial intelligence and specifically to big data and natural language processing technologies. A specific implementation solution is: acquiring an updated question-answer set; comparing blocks of the updated question-answer set with blocks of an original question-answer set in terms of question-answer pairs to determine an unchanged block and a changed block; acquiring feature data of questions included in the changed block, and creating an index file corresponding to the block, and adding the feature data to an updated training output set; and retaining the index file and feature data corresponding to the unchanged block, and adding the feature data to the updated training output set. The present disclosure can reduce the time consumed in the updating process and occupation of resources.

Abstract: The present disclosure discloses a method for generating a question answering robot, relates to the field of robotics. The specific implementation includes: obtaining field information input by a user, obtaining a field-specific robot from a robot library based on the field information; obtaining a template list corresponding to the field-specific robot, providing the template list to the user, the template list including a plurality of templates; receiving the plurality of templates filled in by the user, the templates filled in by the user including at least one question and an answer corresponding to the at least one question; expanding the at least one question filled in by the user based on a question semantic database to form a combination of questions corresponding to the answer, the answer and the combination of questions forming a question-answer pair; and generating a question answering robot based on the question-answer pair.

Abstract: A method for recognizing a dialogue intention, an electronic device and a storage medium are provided. The solution includes: obtaining a sample sentence with an intention category to be predicted and a plurality of supporting sentences each labeled with an intention category; and inputting the sample sentence and the plurality of supporting sentences into a trained intention recognition model, in which the trained intention recognition model is configured to generate a sample sentence vector corresponding to the sample sentence and category vectors corresponding to the plurality of supporting sentences based on the sample sentence and the plurality of supporting sentences, calculate matching degrees between the sample sentence vector and the category vectors, and obtain a predicted intention category of the sample sentence based on the matching degrees.

Abstract: A porous composite material capable of generating an arc in a microwave field includes an inorganic porous framework and a carbon material loaded on the inorganic porous framework. The average pore size of the inorganic porous framework is 0.2-1000 ?m. The porous composite material has an excellent mechanical performance, can generate an arc in a microwave field to quickly generate a high temperature, and thus can be used in fields such as microwave high-temperature heating, biomass pyrolysis, vegetable oil treatment, waste polymer material pyrolysis, petrochemical pyrolysis, carbon-fiber composite material recovery, waste treatment, VOC waste gas treatment, COD wastewater treatment, high-temperature catalysis, waste circuit board full-component recycling, and hydrogen preparation.

Abstract: A method for recognizing a slot, and an electronic device are provided. The technical solution includes: determining each first word in an input sentence and a part of speech each first word; combining each first word in the input sentence based on the parts of speech first words in the input sentence based on the part of speech of each first word to obtain one or more candidate slot segments included in the input sentence; determining a matching degree between each first word in the one or more candidate slot segments and each second word in each reference slot of the slot library; and determining a target slot in the one or more candidate slot segments and a slot name of the target slot based on the matching degree.

Abstract: The present disclosure provides a method, apparatus, and device for recognizing a word slot, and a storage medium, and relates to the technical fields of natural language processing and deep learning. The method may include: receiving a target sentence; determining a first word slot recognition result of the target sentence based on the target sentence and a preset entity set; determining a second word slot recognition result of the target sentence based on the target sentence and a pre-trained word slot recognition model, the word slot recognition model being used to represent a corresponding relationship between the sentence and the word slot recognition result; and determining a target word slot recognition result, based on the first word slot recognition result and the second word slot recognition result.

Abstract: A beam avoidance method comprises: determining, by a first base station, that first UE transmitting a service on a currently scheduled RB is benefited UE, where a cell in which the first UE is located is a first cell; determining, by the first base station, whether a user is satisfied with a service that is of second UE and that is transmitted on the currently scheduled RB, where a cell in which the second UE is located is a second cell, and the second cell is a cell adjacent to the first cell; and adjusting, by the first base station, a transmit weight of the second UE if the user is satisfied with the service that is of the second UE and that is transmitted on the currently scheduled RB.

Abstract: The present disclosure discloses a method for generating a question answering robot, relates to the field of robotics. The specific implementation includes: obtaining field information input by a user, obtaining a field-specific robot from a robot library based on the field information; obtaining a template list corresponding to the field-specific robot, providing the template list to the user, the template list including a plurality of templates; receiving the plurality of templates filled in by the user, the templates filled in by the user including at least one question and an answer corresponding to the at least one question; expanding the at least one question filled in by the user based on a question semantic database to form a combination of questions corresponding to the answer, the answer and the combination of questions forming a question-answer pair; and generating a question answering robot based on the question-answer pair.

Abstract: The present application discloses a method and an apparatus for generating a dialogue, and a storage medium. The method includes: obtaining problem information; inputting the problem information to a small sample learning model to generate a first feature; inputting the problem information to a deep learning (DL) model to generate a second feature; combining the first feature and the second feature to generate a feature sequence; and inputting the feature sequence to a fusion model to generate dialogue information corresponding to the problem information.

Abstract: Embodiments of the present disclosure provide a method and apparatus for outputting analysis abnormality information in a spoken language understanding. The method includes: acquiring first spoken language information and a first analysis result for the first spoken language information; determining whether the first analysis result includes intent information; determining whether the first spoken language information includes a predefined word slot value, in response to determining the first analysis result not including the intent information; and outputting information indicating an existence of an abnormality in the first analysis result, in response to determining the first spoken language information including the predefined word slot value.