QUEST: Quality-Aware Metropolis-Hastings Sampling for Machine Translation
(2406.00049)Abstract
An important challenge in machine translation (MT) is to generate high-quality and diverse translations. Prior work has shown that the estimated likelihood from the MT model correlates poorly with translation quality. In contrast, quality evaluation metrics (such as COMET or BLEURT) exhibit high correlations with human judgments, which has motivated their use as rerankers (such as quality-aware and minimum Bayes risk decoding). However, relying on a single translation with high estimated quality increases the chances of "gaming the metric''. In this paper, we address the problem of sampling a set of high-quality and diverse translations. We provide a simple and effective way to avoid over-reliance on noisy quality estimates by using them as the energy function of a Gibbs distribution. Instead of looking for a mode in the distribution, we generate multiple samples from high-density areas through the Metropolis-Hastings algorithm, a simple Markov chain Monte Carlo approach. The results show that our proposed method leads to high-quality and diverse outputs across multiple language pairs (English$\leftrightarrow${German, Russian}) with two strong decoder-only LLMs (Alma-7b, Tower-7b).
Overview
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The paper proposes a new decoding method for machine translation called Quest, which leverages the Metropolis-Hastings algorithm to generate high-quality and diverse translations.
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Quest outperforms traditional model likelihood approaches and quality-aware reranking methods by using quality metrics as the energy function within a Gibbs distribution framework.
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Experimentation on multiple language pairs demonstrated that Quest improves translation quality and diversity while uncovering unique translation hypotheses not found through conventional methods.
Quest: Quality-Aware Metropolis-Hastings Sampling for Machine Translation
"Quest: Quality-Aware Metropolis-Hastings Sampling for Machine Translation" is a research paper that advances the field of machine translation (MT) by proposing a novel decoding method based on the Metropolis-Hastings algorithm. The primary goal addressed by this study is to generate high-quality and diverse translations, an area where traditional approaches, including model likelihood-based methods and reranking techniques, fall short. By specifically leveraging quality evaluation metrics as energy functions within a Gibbs distribution framework, the proposed methodology, termed Quest, offers a promising alternative to existing MT decoding techniques.
Key Contributions and Methodology
The paper introduces Quest, an innovative approach that employs the Metropolis-Hastings algorithm to sample from high-density areas of the translation space. Previous work demonstrated that maximizing model likelihood often fails to produce translations of high quality due to overly peaked distributions and a singular focus on the most probable sentence. Conversely, while quality-aware reranking and minimum Bayes risk decoding improve translation quality by leveraging metrics like COMET or BLEURT, they can result in overfitting and reduced diversity.
Quest circumvents these issues by using quality metrics as the energy function of a Gibbs distribution. Instead of searching for a single highest-quality translation, Quest generates multiple samples, thus ensuring diversity and mitigating the risks of model overfitting. The approach can be summarized in the following steps:
- Energy Function and Gibbs Distribution: Quality metrics are used as the energy function of a Gibbs distribution to guide the sampling process.
- Metropolis-Hastings Algorithm: Samples are generated from the high-density regions of the distribution using Metropolis-Hastings, a Markov chain Monte Carlo (MCMC) approach.
- Proposal Distribution: A novel proposal distribution is introduced, capable of handling sentence-level evaluation metrics and generating valid text with high diversity.
Results and Experimentation
Quest was benchmarked on multiple language pairs (English↔German, English↔Russian) using two decoder-only LLMs, Alma-7b and Tower-7b. The empirical results are notable for several reasons:
- Quality and Diversity: Quest consistently outperformed ancestral sampling in terms of both translation quality (measured by xComet-XL) and diversity (assessed using average pairwise BLEU scores).
- Convergence: The average quality of translations improved as the number of MCMC steps increased, highlighting the efficacy of the algorithm in exploring high-quality regions of the translation space.
- Novelty of Hypotheses: Quest generated many unique hypotheses that were not present in large pools of translations obtained via ancestral sampling, demonstrating its ability to uncover less probable but high-quality translations.
Theoretical and Practical Implications
Theoretically, the work bridges the gap between statistical sampling methods and practical MT quality estimation. By reformulating the problem of MT as a sampling problem guided by quality metrics, Quest offers a new lens through which translation tasks can be approached. This has implications for other NLP tasks where output quality can significantly benefit from robust sampling methodologies.
Practically, Quest holds promise for applications requiring high-quality translations, such as in medical and legal domains where accuracy is paramount. Furthermore, the method's agnosticism to the specific quality metric means it can adapt to and benefit from future advancements in quality estimation techniques.
Future Prospects
Quest's reliance on sequential sampling for each input prompt introduces a computational overhead, especially for time-sensitive applications. Future research could aim to develop parallel chains or incorporate better initialization strategies to mitigate this. Additionally, refining the proposal distribution to handle longer sequences or document-level translations could further enhance Quest’s applicability.
Moreover, as the field of automatic quality estimation metrics evolves, Quest can directly leverage these improvements, potentially leading to even higher translation quality. Extending this methodology to other NLP tasks, such as summarization or dialogue generation, represents an intriguing avenue for future exploration.
Conclusion
"Quest: Quality-Aware Metropolis-Hastings Sampling for Machine Translation" provides a substantial contribution to the field of machine translation by integrating quality-aware sampling into the decoding process. The use of the Metropolis-Hastings algorithm to generate high-quality, diverse translations marks a significant step forward, with promising implications for future research and applications in AI-driven translation systems.
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