Chen, Y., Zhang, Y., Wang, B., Liu, Z., & Li, H. (2022). Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 8150–8161. https://doi.org/10.18653/v1/2022.emnlp-main.558
Abstract:
Most sentence embedding techniques heavily rely on expensive human-annotated sentence pairs as the supervised signals. Despite the use of large-scale unlabeled data, the performance of unsupervised methods typically lags far behind that of the supervised counterparts in most downstream tasks. In this work, we propose a semi-supervised sentence embedding framework, GenSE, that effectively leverages large-scale unlabeled data. Our method include three parts: 1) Generate: A generator/discriminator model is jointly trained to synthesize sentence pairs from open-domain unlabeled corpus; 2) Discriminate: Noisy sentence pairs are filtered out by the discriminator to acquire high-quality positive and negative sentence pairs; 3) Contrast: A prompt-based contrastive approach is presented for sentence representation learning with both annotated and synthesized data. Comprehensive experiments show that GenSE achieves an average correlation score of 85.19 on the STS datasets and consistent performance improvement on four domain adaptation tasks, significantly surpassing the state-of-the-art methods and convincingly corroborating its effectiveness and generalization ability.
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Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - National Robotics Program: Human-Robot Interaction Phase I
Grant Reference no. : 1922500054
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Advanced Manufacturing and Engineering (AME) Programmatic Funding Scheme
Grant Reference no. : A18A2b0046
This research / project is supported by the Shenzhen Research Institute of Big Data - NA
Grant Reference no. : T00120220002
This research / project is supported by the National Natural Science Foundation of China - NA
Grant Reference no. : 62106222