National University of Defense Technology

Jianming Zheng

Fei Cai

Honghui Chen

Wanyu Chen

Taihua Shao

Dengfeng Liu

Siyuan Wang

Peihong Li

Xueshan Luo

Mengru Wang

Shixian Liu

Chonghao Chen

Yanying Mao

Yupu Guo

A method and an apparatus for few-shot relation classification and filtering, and a device are provided. The method includes: constructing a coarse-grained filter for filtering an unlabeled corpus to obtain candidate instances with a same entity as a seed instance and with similar semantics to the seed instance; constructing a fine-grained filter for filtering the candidate instances to obtain a candidate instance with a same relation concept as the seed instance; defining the candidate instance as a positive instance set, and defining candidate instances with different relation concepts from the seed instance as a negative sample set; constructing a false positive instance correction module for adjusting and controlling a proportion of the negative sample set used by a classifier during training; training the classifier based on a small number of obtained labeled instances belonging to a newly emerging relation and the adjusted positive instance set and negative sample set.

Method and apparatus for few-shot relation classification and filtering, and device

A method and apparatus for training a few-shot event detection model based on multilingual prompt learning are provided, which includes: acquiring a training data set, applying a multilingual prompt model to any instance to obtain a predicted probability distribution of a trigger tag, so as to obtain a first loss; generating a contrastive instance and a bilingual instance, and performing multilingual prompt and cross-lingual encoding according to the input instance and the bilingual instance by applying the multilingual prompt model to obtain joint event characterization; performing event tag prediction on the joint event characterization by applying a two-level hierarchical prototype network model, and calculating a second loss; performing contrastive learning on respective instances by applying a quaternary contrastive learning module to obtain a third loss; determining a total loss of the few-shot event detection model according to respective losses, and performing model training optimization based on the total loss.

Method and apparatus for training few-shot event detection model based on multilingual prompt learning

A fact validation method including the following steps: a statement to be validated is inputted and a searching is made for the statement to obtain an evidence set of the statement; a hierarchical heterogeneous graph consisting of an entity node, a sentence node and a context node is constructed based on the evidence set; the statement and the evidence set are spliced and a node is initialized to obtain feature representation of the node; the feature representation of the node is updated based on inference according to a propagation direction of a neural network of the node in the hierarchical heterogeneous graph; and an inference path for the updated feature representation of the node is built and a prediction result of the statement is output according to the inference path.

Fact validation method and system, computer device and storage medium

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Hunan, China

Jianming Zheng