The vast area of subjectivity in Natural Language Processing (NLP) poses a
challenge to the solutions typically used in generalized tasks. As exploration
in the scope of generalized NLP is much more advanced, it implies the
tremendous gap that is still to be addressed amongst all feasible tasks where
an opinion, taste, or feelings are inherent, thus creating a need for a
solution, where a data fusion could take place. We have chosen the task of
funniness, as it heavily relies on the sense of humor, which is fundamentally
subjective. Our experiments across five personalized and four generalized
datasets involving several personalized deep neural architectures have shown
that the task of humor detection greatly benefits from the inclusion of
personalized data in the training process. We tested five scenarios of training
data fusion that focused on either generalized (majority voting) or
personalized approaches to humor detection. The best results were obtained for
the setup, in which all available personalized datasets were joined to train
the personalized reasoning model. It boosted the prediction performance by up
to approximately 35% of the macro F1 score. Such a significant gain was
observed for all five personalized test sets. At the same time, the impact of
the model's architecture was much less than the personalization itself. It
seems that concatenating personalized datasets, even with the cost of
normalizing the range of annotations across all datasets, if combined with the
personalized models, results in an enormous increase in the performance of
humor detection.

我们选择了滑稽任务，因为滑稽任务严重依赖于幽默感，它是基本主观的。我们的实验表明，在训练过程中，通过包含个性化数据，滑稽检测的任务获得了极大的益处。

从广义的笑声到个性化的轻笑：发挥数据融合在主观幽默检测中的力量

From Generalized Laughter to Personalized Chuckles: Unleashing the Power  of Data Fusion in Subjective Humor Detection

Recommender systems often face heterogeneous datasets containing highly
personalized historical data of users, where no single model could give the
best recommendation for every user. We observe this ubiquitous phenomenon on
both public and private datasets and address the model selection problem in
pursuit of optimizing the quality of recommendation for each user. We propose a
meta-learning framework to facilitate user-level adaptive model selection in
recommender systems. In this framework, a collection of recommenders is trained
with data from all users, on top of which a model selector is trained via
meta-learning to select the best single model for each user with the
user-specific historical data. We conduct extensive experiments on two public
datasets and a real-world production dataset, demonstrating that our proposed
framework achieves improvements over single model baselines and sample-level
model selector in terms of AUC and LogLoss. In particular, the improvements may
lead to huge profit gain when deployed in online recommender systems.

提出了一种元学习框架来实现基于用户的自适应模型选择， 在公开、生产数据集等数据集上进行了实验证明， 该方法在 AUC 和 LogLoss 等指标上优于单一模型基线和基于样本的模型选择器， 可以在在线推荐系统中带来巨大的利润收益。