Papers by Insoo Chung
Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation (2020.findings-emnlp)
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Insoo Chung, Byeongwook Kim, Yoonjung Choi, Se Jung Kwon, Yongkweon Jeon, Baeseong Park, Sangha Kim, Dongsoo Lee
| Challenge: | Quantization is an effective technique to address heavy computation load and memory overhead during inference. |
| Approach: | They propose a low-bit quantization strategy to represent Transformer weights by an extremely low number of bits. |
| Outcome: | The proposed model achieves 11.8 smaller model size than baseline model, with less than -0.5 BLEU. |
Look Harder: A Neural Machine Translation Model with Hard Attention (P19-1)
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| Challenge: | Soft-attention based Neural Machine Translation models attend all the words in the source sequence for each target token, which makes them ineffective for long sequence translation. |
| Approach: | They propose a hard-attention based NMT model which selects a subset of source tokens for each target token to effectively handle long sequence translation. |
| Outcome: | The proposed model performs better on long sequences and achieves significant improvement on English-German and English-French translation tasks compared to soft-attention based models. |