SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup

By Rongzhi Zhang, Yue Yu, Chao Zhang. 2020

104 citations recorded by the source corpus.

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 this https URL

Subfields: Artificial Intelligence, Representation & Self-Supervised Learning

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Citations in the corpus

A Survey of Data Augmentation Approaches for NLP (2021, methodology): “Zhang et al. (2020) propose a DA method based on MIXUP called SEQMIX for active sequence labeling by augmenting queried samples, showing improvements on NER and Event Detection.”
Towards Domain-Agnostic Contrastive Learning (2020, background): “, 2019), natural language (Guo et al., 2019; Zhang et al., 2020) and speech (Lam et al.”