title: STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation
abstract: How to learn a better speech representation for end-to-end speech-to-text translation (ST) with limited labeled data? Existing techniques often attempt to transfer powerful machine translation (MT) capabilities to ST, but neglect the representation discrepancy across modalities. In this paper, we propose the Speech-TExt Manifold Mixup (STEMM) method to calibrate such discrepancy. Specifically, we mix up the representation sequences of different modalities, and take both unimodal speech sequences
journal: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)