Hyperparameter-Free Approach for Faster Minimum Bayes Risk Decoding
Yuu Jinnai Kaito Ariu
Minimum Bayes-Risk (MBR) decoding is shown to be a powerful alternative to
beam search decoding for a wide range of text generation tasks. However, MBR
requires a huge amount of time for inference to compute the MBR objective,
which makes the method infeasible in many situations where response time is
critical. Confidence-based pruning (CBP) (Cheng and Vlachos, 2023) has recently
been proposed to reduce the inference time in machine translation tasks.
Although it is shown to significantly reduce the amount of computation, it
requires hyperparameter tuning using a development set to be effective. To this
end, we propose Approximate Minimum Bayes-Risk (AMBR) decoding, a
hyperparameter-free method to run MBR decoding approximately. AMBR is derived
from the observation that the problem of computing the sample-based MBR
objective is the medoid identification problem. AMBR uses the Correlated
Sequential Halving (CSH) algorithm (Baharav and Tse, 2019), the best
approximation algorithm to date for the medoid identification problem, to
compute the sample-based MBR objective. We evaluate AMBR on machine
translation, text summarization, and image captioning tasks. The results show
that AMBR achieves on par with CBP, with CBP selecting hyperparameters through
an Oracle for each given computation budget.