SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence Representations
This work addresses the need for better sequence representations in NLP, particularly for classification tasks, though it is incremental as it adapts existing supervised contrastive learning to a new domain.
The paper tackles the problem of improving sequence representations in NLP by introducing SupCL-Seq, a supervised contrastive learning method that extends techniques from computer vision, resulting in absolute improvements of up to 6% on GLUE benchmark tasks like CoLA and MRPC compared to standard BERTbase.
While contrastive learning is proven to be an effective training strategy in computer vision, Natural Language Processing (NLP) is only recently adopting it as a self-supervised alternative to Masked Language Modeling (MLM) for improving sequence representations. This paper introduces SupCL-Seq, which extends the supervised contrastive learning from computer vision to the optimization of sequence representations in NLP. By altering the dropout mask probability in standard Transformer architectures, for every representation (anchor), we generate augmented altered views. A supervised contrastive loss is then utilized to maximize the system's capability of pulling together similar samples (e.g., anchors and their altered views) and pushing apart the samples belonging to the other classes. Despite its simplicity, SupCLSeq leads to large gains in many sequence classification tasks on the GLUE benchmark compared to a standard BERTbase, including 6% absolute improvement on CoLA, 5.4% on MRPC, 4.7% on RTE and 2.6% on STSB. We also show consistent gains over self supervised contrastively learned representations, especially in non-semantic tasks. Finally we show that these gains are not solely due to augmentation, but rather to a downstream optimized sequence representation. Code: https://github.com/hooman650/SupCL-Seq