National University of Defense Technology

Peihong Li

Fei Cai

Dengfeng Liu

Siyuan Wang

Jianming Zheng

Honghui Chen

Wanyu Chen

Taihua Shao

Xueshan Luo

Mengru Wang

Shixian Liu

Yanying Mao

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

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Hunan, China

Peihong Li