Beijing Baidu Netcom Science Technology Co., Ltd

Beijing Baidu Netcom Science and Technology Co., Ltd.

Yue Chang

Tingting Li

Shuqi Sun

Zhenyu Jiao

Hongyang Zhang

Haifeng Wang

Hua Wu

Shuai Zhang

Xinyan Xiao

Hao Li

Lijie Wang

Hehan Li

Huifeng Sun

Weijie Ren

Hongjie Guo

Jianglu Hu

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

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

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.

Method for generating dialogue, 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 provides a method and apparatus for acquiring a pre-trained model, an electronic device and a storage medium, and relates to the field of artificial intelligence, such as the natural language processing field, the deep learning field, or the like. The method may include: adding, in a process of training a pre-trained model using training sentences, a learning objective corresponding to syntactic information for a self-attention module in the pre-trained model; and training the pre-trained model according to the defined learning objective. The solution of the present disclosure may improve a performance of the pre-trained model, and reduce consumption of computing resources, or the like.

Method and apparatus for acquiring pre-trained model, electronic device and storage medium

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Beijing, China

Yue Chang