论文标题
自然语言理解中的几个射击插槽标记的矢量投影网络
Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding
论文作者
论文摘要
几乎没有射击的插槽标记对会话对话系统的巨大发展引起的快速领域转移和适应性吸引人。在本文中，我们提出了一个用于几个插槽标签的向量投影网络，该网络利用了每个目标标签向量上的上下文单词嵌入的投影作为单词标签相似性。本质上，这种方法等于具有自适应偏置的归一化线性模型。对比实验表明，我们提出的基于矢量投影的相似性度量可以显着超过其他变体。具体来说，在基准剪切和NER上的五杆设置中，我们的方法的表现分别优于最强的几杆学习基线，分别为$ 6.30 $和13.79美元的$ _1 $得分。我们的代码将在https://github.com/sz128/few_shot_slot_tagging_and_ner上发布。
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.
