The discrepancy between maximum likelihood estimation (MLE) and task measures
such as BLEU score has been studied before for autoregressive neural machine
translation (NMT) and resulted in alternative training algorithms (Ranzato et
al., 2016; Norouzi et al., 2016; Shen et al., 2016; Wu et al., 2018). However,
MLE training remains the de facto approach for autoregressive NMT because of
its computational efficiency and stability. Despite this mismatch between the
training objective and task measure, we notice that the samples drawn from an
MLE-based trained NMT support the desired distribution -- there are samples
with much higher BLEU score comparing to the beam decoding output. To benefit
from this observation, we train an energy-based model to mimic the behavior of
the task measure (i.e., the energy-based model assigns lower energy to samples
with higher BLEU score), which is resulted in a re-ranking algorithm based on
the samples drawn from NMT: energy-based re-ranking (EBR). We use both marginal
energy models (over target sentence) and joint energy models (over both source
and target sentences). Our EBR with the joint energy model consistently
improves the performance of the Transformer-based NMT: +4 BLEU points on
IWSLT'14 German-English, +3.0 BELU points on Sinhala-English, +1.2 BLEU on
WMT'16 English-German tasks.

通过基于能量的重新排序算法，利用基于 MLE 的 NMT 生成更高 BLEU 分数的样本，实现了比 MLE 更好的性能。

基于能量的再排序：利用能量模型来提高神经机器翻译的效果

Energy-Based Reranking: Improving Neural Machine Translation Using  Energy-Based Models

Recommender systems have been applied successfully in a number of different
domains, such as, entertainment, commerce, and employment. Their success lies
in their ability to exploit the collective behavior of users in order to
deliver highly targeted, personalized recommendations. Given that recommenders
learn from user preferences, they incorporate different biases that users
exhibit in the input data. More importantly, there are cases where recommenders
may amplify such biases, leading to the phenomenon of bias disparity. In this
short paper, we present a preliminary experimental study on synthetic data,
where we investigate different conditions under which a recommender exhibits
bias disparity, and the long-term effect of recommendations on data bias. We
also consider a simple re-ranking algorithm for reducing bias disparity, and
present some observations for data disparity on real data.

本研究在合成数据上进行了初步的实验研究，在不同条件下研究推荐系统如何表现出偏见失衡现象以及推荐的长期效果。我们考虑了一种简单的重新排名算法来减少偏见失衡，并对实际数据的数据失衡进行了一些观察。

Bias Disparity in Recommendation Systems

Personalized recommendation brings about novel challenges in ensuring
fairness, especially in scenarios in which users are not the only stakeholders
involved in the recommender system. For example, the system may want to ensure
that items from different providers have a fair chance of being recommended. To
solve this problem, we propose a Fairness-Aware Re-ranking algorithm (FAR) to
balance the ranking quality and provider-side fairness. We iteratively generate
the ranking list by trading off between accuracy and the coverage of the
providers. Although fair treatment of providers is desirable, users may differ
in their receptivity to the addition of this type of diversity. Therefore,
personalized user tolerance towards provider diversification is incorporated.
Experiments are conducted on both synthetic and real-world data. The results
show that our proposed re-ranking algorithm can significantly promote fairness
with a slight sacrifice in accuracy and can do so while being attentive to
individual user differences.

本篇研究提出了一种考虑到用户、排名质量和供应商公平性的新型推荐算法 FRA，在实验中，该算法在牺牲一定精度的情况下，有效提升了推荐系统的公平性，并考虑到了个体用户的偏好。