Align-to-Distill: Trainable Attention Alignment for Knowledge Distillation in Neural Machine Translation

• URL: http://arxiv.org/abs/2403.01479v3
• Date: Mon, 25 Mar 2024 08:46:15 GMT
• Title: Align-to-Distill: Trainable Attention Alignment for Knowledge Distillation in Neural Machine Translation
• Authors: Heegon Jin, Seonil Son, Jemin Park, Youngseok Kim, Hyungjong Noh, Yeonsoo Lee,
• Abstract summary: We introduce the 'Align-to-Distill' (A2D) strategy to address the feature mapping problem. Our experiments show the efficacy of A2D, demonstrating gains of up to +3.61 and +0.63 BLEU points for WMT-2022->Dsb and WMT-2014 En->De.
• Score: 3.759878064139572
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The advent of scalable deep models and large datasets has improved the performance of Neural Machine Translation. Knowledge Distillation (KD) enhances efficiency by transferring knowledge from a teacher model to a more compact student model. However, KD approaches to Transformer architecture often rely on heuristics, particularly when deciding which teacher layers to distill from. In this paper, we introduce the 'Align-to-Distill' (A2D) strategy, designed to address the feature mapping problem by adaptively aligning student attention heads with their teacher counterparts during training. The Attention Alignment Module in A2D performs a dense head-by-head comparison between student and teacher attention heads across layers, turning the combinatorial mapping heuristics into a learning problem. Our experiments show the efficacy of A2D, demonstrating gains of up to +3.61 and +0.63 BLEU points for WMT-2022 De->Dsb and WMT-2014 En->De, respectively, compared to Transformer baselines.

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