Query-Key Normalization for Transformers
Alex Henry, Prudhvi Raj Dachapally, Shubham Shantaram Pawar, Yuxuan Chen
Abstract
Low-resource language translation is a challenging but socially valuable NLP task. Building on recent work adapting the Transformer’s normalization to this setting, we propose QKNorm, a normalization technique that modifies the attention mechanism to make the softmax function less prone to arbitrary saturation without sacrificing expressivity. Specifically, we apply l2-normalization along the head dimension of each query and key matrix prior to multiplying them and then scale up by a learnable parameter instead of dividing by the square root of the embedding dimension. We show improvements averaging 0.928 BLEU over state-of-the-art bilingual benchmarks for 5 low-resource translation pairs from the TED Talks corpus and IWSLT’15.- Anthology ID:
- 2020.findings-emnlp.379
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4246–4253
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.379
- DOI:
- 10.18653/v1/2020.findings-emnlp.379
- Cite (ACL):
- Alex Henry, Prudhvi Raj Dachapally, Shubham Shantaram Pawar, and Yuxuan Chen. 2020. Query-Key Normalization for Transformers. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4246–4253, Online. Association for Computational Linguistics.
- Cite (Informal):
- Query-Key Normalization for Transformers (Henry et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.findings-emnlp.379.pdf
- Code
- CyndxAI/QKNorm + additional community code