Linear-time Minimum Bayes Risk Decoding with Reference Aggregation
(2402.04251)Abstract
Minimum Bayes Risk (MBR) decoding is a text generation technique that has been shown to improve the quality of machine translations, but is expensive, even if a sampling-based approximation is used. Besides requiring a large number of sampled sequences, it requires the pairwise calculation of a utility metric, which has quadratic complexity. In this paper, we propose to approximate pairwise metric scores with scores calculated against aggregated reference representations. This changes the complexity of utility estimation from $O(n2)$ to $O(n)$, while empirically preserving most of the quality gains of MBR decoding. We release our source code at https://github.com/ZurichNLP/mbr
Overview
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This paper introduces a novel reference aggregation approach to improve the computational efficiency of Minimum Bayes Risk (MBR) decoding in neural machine translation.
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By creating an aggregated reference representation for utility metric calculations, time complexity is reduced from quadratic to linear, resulting in substantial computational savings.
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Empirical results show that for CHR F metric, there is a 99.5% reduction in time without losing translation quality, while for COMET, there is a slight trade-off between time efficiency and accuracy.
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The study suggests the potential of reference aggregation in making sophisticated decoding techniques more practical for large-scale applications, but acknowledges limitations and calls for further research.
Background and Methodology
Recent advances in neural machine translation (NMT) have brought forward the concept of Minimum Bayes Risk (MBR) decoding as a powerful method to improve translation quality. Nonetheless, MBR is computationally intensive; it relies on numerous sampled sequences and necessitates pairwise computation of utility metrics, a process with quadratic complexity. In this context, Vamvas and Sennrich have introduced a novel approach that notably reduces this complexity by calculating scores against an aggregated reference representation instead of pairwise.
The proposed method utilizes the fact that many common utility metrics in MBR are based on features that can be averaged—such as n-gram statistics for C HR F or sentence embeddings for COMET. By averaging these features across all references to create a singular aggregate representation, the authors shift the complexity of utility estimation from quadratic (O(n2)) to linear (O(n)), yielding significant computational savings.
Empirical Analysis
The paper presents empirical results obtained by deploying this approach on four translation directions using two utility metrics—CHR F and COMET. Remarkably, with CHR F, the authors achieved an astounding 99.5% reduction in the time required for computing utility across 1024 samples without impacting translation quality. With COMET, they observed a smaller impact on metric accuracy but maintained significant time efficiency, with a reduction of 95-99%.
It is crucial to highlight that while the computed time efficiency is impressive, the authors discern a nuanced trade-off. For COMET, accuracy slightly diminishes with aggregation, although this effect is less substantial than simply limiting the reference count.
Pragmatic Application
The significance of the research extends to practical utility. The authors suggest this method could be a game-changer in rendering MBR decoding more feasible for large-scale applications. They highlight what they term 'reference aggregation' as an effective strategy to tackle the traditionally high computational load of MBR, bringing it closer in terms of efficiency to more commonly used, faster algorithms like beam search.
Limitations and Future Directions
The authors acknowledge certain limitations within their analysis. They note that the viability of reference aggregation depends on the nature of the utility metric in use; not all metrics may support such an approach due to differences in averageability. Their focus on CHR F and COMET is justified by these metrics' compatibility with the proposed method.
Future research, they suggest, should investigate the application of reference aggregation to other trained metrics and different architectures, such as cross-encoder models used by metrics like BLEURT. Additionally, while they have demonstrated the efficiency of reference aggregation, they call for further exploration in the arena of sampling efficiency to possibly achieve even faster MBR decoding.
Conclusion
The paper posits reference aggregation as a proficient technique for cost-efficient MBR decoding without significantly compromising translation quality. It represents an important step towards making sophisticated decoding techniques like MBR more practical for use in resource-intensive NMT tasks.
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