Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding
This work addresses the problem of efficient domain transfer for slot tagging in natural language understanding, offering a method with strong performance gains, though it is incremental in nature.
The paper tackles few-shot slot tagging for rapid domain adaptation in conversational systems by proposing a vector projection network that uses projections of word embeddings on label vectors as similarities, achieving F1 score improvements of 6.30 and 13.79 points over baselines on SNIPS and NER benchmarks in five-shot settings.
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.