| Challenge: | Neural machine translation models are trained using 32-bit floating point values and have improved fluency and adequacy. |
| Approach: | They propose to use 8-bit quantization to train models using 32-bit floating point values and show that 8- bit translation makes a non-negligible impact in terms of speed with no degradation in accuracy and adequacy. |
| Outcome: | The proposed method improves accuracy and accuracy without degradation in accuracy and adequacy. |
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Insoo Chung, Byeongwook Kim, Yoonjung Choi, Se Jung Kwon, Yongkweon Jeon, Baeseong Park, Sangha Kim, Dongsoo Lee
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