Регуляризация согласованности

AbstractFine-tuning pre-trained cross-lingual language models can transfer task-specific supervision from one language to the others. In this work, we propose to improve cross-lingual fine-tuning with consistency regularization. Specifically, we use …...

AbstractIn natural language processing (NLP), state-of-the-art (SOTA) semi-supervised learning (SSL) frameworks have shown great performance on deep pre-trained language models such as BERT, and are expected to significantly reduce the …...

AbstractDocument-level relation extraction (DocRE) aims at extracting relations of all entity pairs in a document. A key challenge to DocRE lies in the complex interdependency between the relations of entity …...

AbstractWhile the prompt-based fine-tuning methods had advanced few-shot natural language understanding tasks, self-training methods are also being explored. This work revisits the consistency regularization in self-training and presents explicit and …...

AbstractExisting neural models have difficulty generalizing to unseen combinations of seen components. To achieve compositional generalization, models are required to consistently interpret (sub)expressions across contexts. Without modifying model architectures, we …...