To be Continuous, or to be Discrete, Those are Bits of Questions
This work addresses the gap between continuous deep learning and discrete natural language properties, but is incremental as it builds on existing binary representation methods.
The paper tackles the problem of using binary representations for structured prediction outputs, extending contrastive hashing to preserve structural and label information, and achieves competitive performance on various tasks.
Recently, binary representation has been proposed as a novel representation that lies between continuous and discrete representations. It exhibits considerable information-preserving capability when being used to replace continuous input vectors. In this paper, we investigate the feasibility of further introducing it to the output side, aiming to allow models to output binary labels instead. To preserve the structural information on the output side along with label information, we extend the previous contrastive hashing method as structured contrastive hashing. More specifically, we upgrade CKY from label-level to bit-level, define a new similarity function with span marginal probabilities, and introduce a novel contrastive loss function with a carefully designed instance selection strategy. Our model achieves competitive performance on various structured prediction tasks, and demonstrates that binary representation can be considered a novel representation that further bridges the gap between the continuous nature of deep learning and the discrete intrinsic property of natural languages.