  Relation classification (RC) task is one of fundamental tasks of information
extraction, aiming to detect the relation information between entity pairs in
unstructured natural language text and generate structured data in the form of
entity-relation triple. Although distant supervision methods can effectively
alleviate the problem of lack of training data in supervised learning, they
also introduce noise into the data, and still cannot fundamentally solve the
long-tail distribution problem of the training instances. In order to enable
the neural network to learn new knowledge through few instances like humans,
this work focuses on few-shot relation classification (FSRC), where a
classifier should generalize to new classes that have not been seen in the
training set, given only a number of samples for each class. To make full use
of the existing information and get a better feature representation for each
instance, we propose to encode each class prototype in an adaptive way from two
aspects. First, based on the prototypical networks, we propose an adaptive
mixture mechanism to add label words to the representation of the class
prototype, which, to the best of our knowledge, is the first attempt to
integrate the label information into features of the support samples of each
class so as to get more interactive class prototypes. Second, to more
reasonably measure the distances between samples of each category, we introduce
a loss function for joint representation learning to encode each support
instance in an adaptive manner. Extensive experiments have been conducted on
FewRel under different few-shot (FS) settings, and the results show that the
proposed adaptive prototypical networks with label words and joint
representation learning has not only achieved significant improvements in
accuracy, but also increased the generalization ability of few-shot RC models.
