Summary of Align-to-distill: Trainable Attention Alignment For Knowledge Distillation in Neural Machine Translation, by Heegon Jin et al.
Align-to-Distill: Trainable Attention Alignment for Knowledge Distillation in Neural Machine Translation
by Heegon Jin, Seonil Son, Jemin Park, Youngseok Kim, Hyungjong Noh, Yeonsoo Lee
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach called ‘Align-to-Distill’ (A2D) that enhances the efficiency of Knowledge Distillation (KD) in Transformer-based Neural Machine Translation. A2D addresses the feature mapping problem by adaptively aligning student attention heads with their teacher counterparts during training, turning combinatorial mapping heuristics into a learning problem. The Attention Alignment Module performs dense head-by-head comparisons between student and teacher attention heads across layers. Experimental results demonstrate 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new approach called ‘Align-to-Distill’ that makes Neural Machine Translation more efficient. It helps smaller models learn from bigger ones by aligning their attention heads. This is done by comparing each head in the smaller model with its counterpart in the bigger one, making sure they’re working together well. The results show that this method can improve translation quality. |
Keywords
» Artificial intelligence » Alignment » Attention » Bleu » Knowledge distillation » Transformer » Translation
