A Modular-based Strategy for Mitigating Gradient Conflicts in Simultaneous Speech Translation

2024-09-24

arXiv.org

AI Summary

In simultaneous speech translation (SimulST), multi-task learning induces gradient conflicts between primary and auxiliary tasks, degrading translation quality and inflating GPU memory consumption. To address this, we propose a module-level gradient conflict detection and projection mitigation mechanism. Unlike conventional model-level approaches, our method performs fine-grained conflict identification and orthogonal gradient projection at the module level, significantly improving optimization efficiency. Integrated into a modular network architecture, it achieves a 0.68 BLEU gain under medium-to-high latency settings while reducing GPU memory usage by over 95%, thus balancing translation quality and real-time performance. To the best of our knowledge, this is the first work to refine gradient conflict mitigation to the module level, establishing a new paradigm for efficient, low-resource SimulST.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Simultaneous Speech Translation (SimulST) involves generating target language text while continuously processing streaming speech input, presenting significant real-time challenges. Multi-task learning is often employed to enhance SimulST performance but introduces optimization conflicts between primary and auxiliary tasks, potentially compromising overall efficiency. The existing model-level conflict resolution methods are not well-suited for this task which exacerbates inefficiencies and leads to high GPU memory consumption. To address these challenges, we propose a Modular Gradient Conflict Mitigation (MGCM) strategy that detects conflicts at a finer-grained modular level and resolves them utilizing gradient projection. Experimental results demonstrate that MGCM significantly improves SimulST performance, particularly under medium and high latency conditions, achieving a 0.68 BLEU score gain in offline tasks. Additionally, MGCM reduces GPU memory consumption by over 95% compared to other conflict mitigation methods, establishing it as a robust solution for SimulST tasks.

Problem

• Simultaneous Speech Translation
• Multi-task Learning Conflict
• Translation Quality Degradation

Innovation

• Modular Gradient Conflict Mitigation (MGCM)
• Simultaneous Speech Translation (SimulST)
• Multi-task Learning Efficiency