少样本文本分类的多任务原型网络

少样本文本分类中，原型网络对语义利用不足、可迁移特征挖掘不够，导致模型泛化能力不强，在新任务空间中分类性能不佳。从模型结构、编码网络、度量网络等角度提高模型泛化性，提出多任务原型网络（multiple-task prototypical network，MTPN）。结构上，基于原型网络度量任务增加辅助分类任务约束训练目标，提高了模型的语义特征抽取能力，利用多任务联合训练，获得与辅助任务更相关的语义表示。针对编码网络，提出LF-Transformer编码器，使用层级注意力融合底层通用编码信息，提升特征的可迁移性。度量网络使用基于BiGRU的类原型生成器，使类原型更具代表性，距离度量更加准确。实验表明，MTPN在少样本文本情感分类任务中取得了91.62%的准确率，比现有最佳模型提升了3.5%以上；在新领域的情感评论中，基于五条参考样本，模型对查询样本可获得超过90%的分类准确率。

Multiple-task prototypical network for few-shot text classification

Since the prototype network cannot make full use of samples'' semantic information, it''s difficult for model to fully excavate the transferable features in training data. As the result, the model underperforms when it is facing unfamiliar data in a new domain. To this end, this paper made improvements from three perspectives: model structure, encoding network, and metric network, and proposed a multiple-task prototypical network MTPN. In terms of model structure, on the basis of the prototype network''s metric task, it added an auxiliary classification task to constrain the training target, which can improve the semantic feature extraction ability of the model. By using multi-task learning, model obtained a semantic representation that was more relevant to the auxiliary task. In order to improve feature transferability, this paper also proposed the LF-Transformer encoder which used hierarchical attention to fuse the underlying general encoding information. The metric network used the BiGRU-based class prototype generator to make the class prototype more representative and the distance measurement more accurate. Experiments show that MTPN achieves an accuracy of 91.62% in the sentiment classification task with few samples, which is 3.5% higher than the existing best model. For samples in new field that have not appeared in training state, by using 5 references, the model can still obtain a classification accuracy of more than 90% on query samples.