Understanding and Bridging the Modality Gap for Speech Translation

TL;DR

This paper investigates the modality gap between speech translation and machine translation, linking it to exposure bias, and proposes a novel regularization method with adaptive training to improve end-to-end speech translation performance.

Contribution

It introduces the Cress method with scheduled sampling and token-level adaptive training to effectively bridge the modality gap in speech translation.

Findings

Cress reduces the modality gap during inference.

The approach improves translation quality across multiple language directions.

Results demonstrate significant gains on the MuST-C dataset.

Abstract

How to achieve better end-to-end speech translation (ST) by leveraging (text) machine translation (MT) data? Among various existing techniques, multi-task learning is one of the effective ways to share knowledge between ST and MT in which additional MT data can help to learn source-to-target mapping. However, due to the differences between speech and text, there is always a gap between ST and MT. In this paper, we first aim to understand this modality gap from the target-side representation differences, and link the modality gap to another well-known problem in neural machine translation: exposure bias. We find that the modality gap is relatively small during training except for some difficult cases, but keeps increasing during inference due to the cascading effect. To address these problems, we propose the Cross-modal Regularization with Scheduled Sampling (Cress) method. Specifically, we regularize the output predictions of ST and MT, whose target-side contexts are derived by sampling between ground truth words and self-generated words with a varying probability. Furthermore, we introduce token-level adaptive training which assigns different training weights to target tokens to handle difficult cases with large modality gaps. Experiments and analysis show that our approach effectively bridges the modality gap, and achieves promising results in all eight directions of the MuST-C dataset.