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Creators/Authors contains: "Yang, Xiaocong Yang"

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  1. ; ; ; (, Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL))
    Vlachos, Andreas; Augenstein, Isabelle (Ed.)
    Parameter-efficient tuning aims at updating only a small subset of parameters when adapting a pretrained model to downstream tasks. In this work, we introduce PASTA, in which we only modify the special token representations (e.g., [SEP] and [CLS] in BERT) before the self-attention module at each layer in Transformer-based models. PASTA achieves comparable performance to fine-tuning in natural language understanding tasks including text classification and NER with up to only 0.029% of total parameters trained. Our work not only provides a simple yet effective way of parameter-efficient tuning, which has a wide range of practical applications when deploying finetuned models for multiple tasks, but also demonstrates the pivotal role of special tokens in pretrained language models. 
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    Accepted Manuscript Full Text Available