Many discriminative natural language understanding (NLU) tasks have large
label spaces. Learning such a process of large-space decision making is
particularly challenging due to the lack of training instances per label and
the difficulty of selection among many fine-grained labels. Inspired by dense
retrieval methods for passage finding in open-domain QA, we propose a
reformulation of large-space discriminative NLU tasks as a learning-to-retrieve
task, leading to a novel solution named Dense Decision Retrieval (DDR ).
Instead of predicting fine-grained decisions as logits, DDR adopts a
dual-encoder architecture that learns to predict by retrieving from a decision
thesaurus. This approach not only leverages rich indirect supervision signals
from easy-to-consume learning resources for dense retrieval, it also leads to
enhanced prediction generalizability with a semantically meaningful
representation of the large decision space. When evaluated on tasks with
decision spaces ranging from hundreds to hundred-thousand scales, DDR
outperforms strong baselines greatly by 27.54% in P@1 on two extreme
multi-label classification tasks, 1.17% in F1 score ultra-fine entity typing,
and 1.26% in accuracy on three few-shot intent classification tasks on average.
Code and resources are available at this https URL

通过将大空间的判别式自然语言理解任务重新构造为学习检索任务，采用密集检索方法来解决大决策空间的预测问题，提高了预测的泛化能力和决策表示的语义意义。