Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding

09/21/2020
by   Su Zhu, et al.
0

Few-shot slot tagging becomes appealing for rapid domain transfer and adaptation, motivated by the tremendous development of conversational dialogue systems. In this paper, we propose a vector projection network for few-shot slot tagging, which exploits projections of contextual word embeddings on each target label vector as word-label similarities. Essentially, this approach is equivalent to a normalized linear model with an adaptive bias. The contrastive experiment demonstrates that our proposed vector projection based similarity metric can significantly surpass other variants. Specifically, in the five-shot setting on benchmarks SNIPS and NER, our method outperforms the strongest few-shot learning baseline by 6.30 and 13.79 points on F_1 score, respectively. Our code will be released at https://github.com/sz128/few_shot_slot_tagging_and_NER.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset