Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Zhenyu Jiao

Tingting Li

Shuqi Sun

Yue Chang

Ke Wei Sun

Hongjie Guo

Hongyang Zhang

Lei Han

Hao Li

Weijie Ren

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.

Method for processing question, electronic device and storage medium

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.

Model training method and method for human-machine interaction

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.

Method and apparatus for creating dialogue, and storage medium

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.

Semantic understanding method, electronic device, and storage medium

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.

Method for recognizing dialogue intention, electronic device and storage medium

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.

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Beijing, China

Zhenyu Jiao