  Active learning is an important technique for low-resource sequence labeling
tasks. However, current active sequence labeling methods use the queried
samples alone in each iteration, which is an inefficient way of leveraging
human annotations. We propose a simple but effective data augmentation method
to improve the label efficiency of active sequence labeling. Our method,
SeqMix, simply augments the queried samples by generating extra labeled
sequences in each iteration. The key difficulty is to generate plausible
sequences along with token-level labels. In SeqMix, we address this challenge
by performing mixup for both sequences and token-level labels of the queried
samples. Furthermore, we design a discriminator during sequence mixup, which
judges whether the generated sequences are plausible or not. Our experiments on
Named Entity Recognition and Event Detection tasks show that SeqMix can improve
the standard active sequence labeling method by $2.27\%$--$3.75\%$ in terms of
$F_1$ scores. The code and data for SeqMix can be found at
https://github.com/rz-zhang/SeqMix
