Scoring the driving performance of various drivers on a unified scale, based
on how safe or economical they drive on their daily trips, is essential for the
driver profile task. Connected vehicles provide the opportunity to collect
real-world driving data, which is advantageous for constructing scoring models.
However, the lack of pre-labeled scores impede the use of supervised regression
models and the data privacy issues hinder the way of traditionally
data-centralized learning on the cloud side for model training. To address
them, an unsupervised scoring method is presented without the need for labels
while still preserving fairness and objectiveness compared to subjective
scoring strategies. Subsequently, a federated learning framework based on
vehicle-cloud collaboration is proposed as a privacy-friendly alternative to
centralized learning. This framework includes a consistently federated version
of the scoring method to reduce the performance degradation of the global
scoring model caused by the statistical heterogeneous challenge of local data.
Theoretical and experimental analysis demonstrate that our federated scoring
model is consistent with the utility of the centrally learned counterpart and
is effective in evaluating driving performance.

基于联网汽车与云端协作的联邦学习方法和无监督评分模型，能够在不需要标签的情况下准确评估驾驶员的驾驶表现。

FedDriveScore: 分布式评分驾驶行为的联邦评分

FedDriveScore: Federated Scoring Driving Behavior with a Mixture of  Metric Distributions

Fine-tuning of pre-trained transformer models has become the standard
approach for solving common NLP tasks. Most of the existing approaches rely on
a randomly initialized classifier on top of such networks. We argue that this
fine-tuning procedure is sub-optimal as the pre-trained model has no prior on
the specific classifier labels, while it might have already learned an
intrinsic textual representation of the task. In this paper, we introduce a new
scoring method that casts a plausibility ranking task in a full-text format and
leverages the masked language modeling head tuned during the pre-training
phase. We study commonsense reasoning tasks where the model must rank a set of
hypotheses given a premise, focusing on the COPA, Swag, HellaSwag and
CommonsenseQA datasets. By exploiting our scoring method without fine-tuning,
we are able to produce strong baselines (e.g. 80% test accuracy on COPA) that
are comparable to supervised approaches. Moreover, when fine-tuning directly on
the proposed scoring function, we show that our method provides a much more
stable training phase across random restarts (e.g $\times 10$ standard
deviation reduction on COPA test accuracy) and requires less annotated data
than the standard classifier approach to reach equivalent performances.

本研究提出一种基于预训练 transformer 模型的全文格式的可信度排名得分方法，无需微调即可产生强大的基线，可以为通识推理任务提供更加稳定和有效的训练解决方案。

预训练几乎就是你所需要的：应用于常识推理

Pre-training Is (Almost) All You Need: An Application to Commonsense  Reasoning

Large-scale dialogue datasets have recently become available for training
neural dialogue agents. However, these datasets have been reported to contain a
non-negligible number of unacceptable utterance pairs. In this paper, we
propose a method for scoring the quality of utterance pairs in terms of their
connectivity and relatedness. The proposed scoring method is designed based on
findings widely shared in the dialogue and linguistics research communities. We
demonstrate that it has a relatively good correlation with the human judgment
of dialogue quality. Furthermore, the method is applied to filter out
potentially unacceptable utterance pairs from a large-scale noisy dialogue
corpus to ensure its quality. We experimentally confirm that training data
filtered by the proposed method improves the quality of neural dialogue agents
in response generation.

本文提出了一种基于对话和语言学研究共同发现的相关性和连接性评分方法，用于评估大规模对话数据集中话语对的质量，并过滤掉潜在的不可接受的话语对，以提高神经对话代理的响应生成能力。

通过连通性和内容相关性过滤嘈杂的对话语料库

Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness

Manually grading the Response to Text Assessment (RTA) is labor intensive.
Therefore, an automatic method is being developed for scoring analytical
writing when the RTA is administered in large numbers of classrooms. Our
long-term goal is to also use this scoring method to provide formative feedback
to students and teachers about students' writing quality. As a first step
towards this goal, interpretable features for automatically scoring the
evidence rubric of the RTA have been developed. In this paper, we present a
simple but promising method for improving evidence scoring by employing the
word embedding model. We evaluate our method on corpora of responses written by
upper elementary students.

本文研究旨在开发通过自动方法对大量课堂的 RTA 进行分数评估，以提供给学生和老师有关学生写作质量的形成性反馈。为了实现这个目标，我们开发了可解释特征自动评估 RTA 的证据评分。本文提出了一个简单而有前途的方法，通过利用单词嵌入模型来提高证明评分。我们在小学生写作语料库中评估了这种方法。