Recent literature has seen a significant focus on building machine learning
models with specific properties such as fairness, i.e., being non-biased with
respect to a given set of attributes, calibration i.e., model confidence being
aligned with its predictive accuracy, and explainability, i.e., ability to be
understandable to humans. While there has been work focusing on each of these
aspects individually, researchers have shied away from simultaneously
addressing more than one of these dimensions. In this work, we address the
problem of building models which are both fair and calibrated. We work with a
specific definition of fairness, which closely matches [Biswas et. al. 2019],
and has the nice property that Bayes optimal classifier has the maximum
possible fairness under our definition. We show that an existing negative
result towards achieving a fair and calibrated model [Kleinberg et. al. 2017]
does not hold for our definition of fairness. Further, we show that ensuring
group-wise calibration with respect to the sensitive attributes automatically
results in a fair model under our definition. Using this result, we provide a
first cut approach for achieving fair and calibrated models, via a simple
post-processing technique based on temperature scaling. We then propose
modifications of existing calibration losses to perform group-wise calibration,
as a way of achieving fair and calibrated models in a variety of settings.
Finally, we perform extensive experimentation of these techniques on a diverse
benchmark of datasets, and present insights on the pareto-optimality of the
resulting solutions.

在这项研究中，我们提出了一种同时具备公平性和校准性的模型构建方法，通过基于温度缩放的简单后处理技术来实现，同时对现有的校准损失函数进行修改以进行组内校准，从而在多种场景下实现公平和校准的模型。

走向公正和校准的模型

Towards Fair and Calibrated Models

Ultra-fine entity typing (UFET) is the task of inferring the semantic types,
from a large set of fine-grained candidates, that apply to a given entity
mention. This task is especially challenging because we only have a small
number of training examples for many of the types, even with distant
supervision strategies. State-of-the-art models, therefore, have to rely on
prior knowledge about the type labels in some way. In this paper, we show that
the performance of existing methods can be improved using a simple technique:
we use pre-trained label embeddings to cluster the labels into semantic domains
and then treat these domains as additional types. We show that this strategy
consistently leads to improved results, as long as high-quality label
embeddings are used. We furthermore use the label clusters as part of a simple
post-processing technique, which results in further performance gains. Both
strategies treat the UFET model as a black box and can thus straightforwardly
be used to improve a wide range of existing models.

本文提出了一种简单的技术，使用预训练的标签嵌入将标签聚类形成语义领域，并将这些领域作为附加类型来改进现有的 UFET 模型的性能。同时作者还使用标签聚类作为后处理技术，进一步提高了模型性能。

利用标签的先验知识进行细粒度实体类型划分：一种简单的基于聚类的策略

Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple  Clustering Based Strategy

Detecting and recovering out-of-vocabulary (OOV) words is always challenging
for Automatic Speech Recognition (ASR) systems. Many existing methods focus on
modeling OOV words by modifying acoustic and language models and integrating
context words cleverly into models. To train such complex models, we need a
large amount of data with context words, additional training time, and
increased model size. However, after getting the ASR transcription to recover
context-based OOV words, the post-processing method has not been explored much.
In this work, we propose a post-processing technique to improve the performance
of context-based OOV recovery. We created an acoustically boosted language
model with a sub-graph made at phone level with an OOV words list. We proposed
two methods to determine a suitable cost function to retrieve the OOV words
based on the context. The cost function is defined based on phonetic and
acoustic knowledge for matching and recovering the correct context words in the
decode. The effectiveness of the proposed cost function is evaluated at both
word-level and sentence-level. The evaluation results show that this approach
can recover an average of 50% context-based OOV words across multiple
categories.

本文提出了一种基于声学增强的语言模型的后处理方法，通过基于语境和音素知识来匹配和恢复正确的上下文单词，该方法可以在多个类别中恢复平均 50% 的基于上下文的未登录词。