We present ELSA, a practical solution for creating deep networks that can
easily be deployed at different levels of sparsity. The core idea is to embed
one or more sparse networks within a single dense network as a proper subset of
the weights. At prediction time, any sparse model can be extracted effortlessly
simply be zeroing out weights according to a predefined mask. ELSA is simple,
powerful and highly flexible. It can use essentially any existing technique for
network sparsification and network training. In particular, it does not
restrict the loss function, architecture or the optimization technique. Our
experiments show that ELSA's advantages of flexible deployment comes with no or
just a negligible reduction in prediction quality compared to the standard way
of using multiple sparse networks that are trained and stored independently.

ELSA 是一个实用的解决方案，可以在不同稀疏级别上轻松部署的深度网络。在预测时，只需根据预定义的掩码将权重清零，即可轻松提取任何稀疏模型，ELSA 具有简单、强大和高度灵活的特点，可以使用几乎任何现有的网络稀疏化和网络训练技术。实验证明，与独立训练和存储的多个稀疏网络相比，ELSA 在灵活部署方面的优势几乎不会或只会带来可忽略的预测质量下降。

ELSA：无额外开销的稀疏网络部署中的部分权重冻结

ELSA: Partial Weight Freezing for Overhead-Free Sparse Network  Deployment

We study the domain adaptation problem with label shift in this work. Under
the label shift context, the marginal distribution of the label varies across
the training and testing datasets, while the conditional distribution of
features given the label is the same. Traditional label shift adaptation
methods either suffer from large estimation errors or require cumbersome
post-prediction calibrations. To address these issues, we first propose a
moment-matching framework for adapting the label shift based on the geometry of
the influence function. Under such a framework, we propose a novel method named
\underline{E}fficient \underline{L}abel \underline{S}hift
\underline{A}daptation (ELSA), in which the adaptation weights can be estimated
by solving linear systems. Theoretically, the ELSA estimator is
$\sqrt{n}$-consistent ($n$ is the sample size of the source data) and
asymptotically normal. Empirically, we show that ELSA can achieve
state-of-the-art estimation performances without post-prediction calibrations,
thus, gaining computational efficiency.

本文探讨如何解决标签偏移的域自适应问题，提出了基于影响函数几何的时刻匹配框架，讨论了一种名为 ELSA 的解决方案，并通过大量实验证明该方法在无需繁琐的后验校准的情况下，能达到与最先进方法一样的性能并获得计算效率的提升。

ELSA: 通过半参数模型视角实现有效标签偏移适应

ELSA: Efficient Label Shift Adaptation through the Lens of  Semiparametric Models

This paper explores the task of identifying the overall sentiment expressed
towards volitional entities (persons and organizations) in a document -- what
we refer to as Entity-Level Sentiment Analysis (ELSA). While identifying
sentiment conveyed towards an entity is well researched for shorter texts like
tweets, we find little to no research on this specific task for longer texts
with multiple mentions and opinions towards the same entity. This lack of
research would be understandable if ELSA can be derived from existing tasks and
models. To assess this, we annotate a set of professional reviews for their
overall sentiment towards each volitional entity in the text. We sample from
data already annotated for document-level, sentence-level, and target-level
sentiment in a multi-domain review corpus, and our results indicate that there
is no single proxy task that provides this overall sentiment we seek for the
entities at a satisfactory level of performance. We present a suite of
experiments aiming to assess the contribution towards ELSA provided by
document-, sentence-, and target-level sentiment analysis, and provide a
discussion of their shortcomings. We show that sentiment in our dataset is
expressed not only with an entity mention as target, but also towards targets
with a sentiment-relevant relation to a volitional entity. In our data, these
relations extend beyond anaphoric coreference resolution, and our findings call
for further research of the topic. Finally, we also present a survey of
previous relevant work.

本篇研究探讨了实体级情感分析（Entity-Level Sentiment Analysis，ELSA）这一任务，通过对多个领域的专业评论进行深入研究，发现其中所包含的情感涉及的实体与情感之间存在着特定关系，并提供了一系列实验以证明文档级、句子级和目标级情感分析对于 ELSA 的贡献，并探讨了它们的缺点。同时，还对该领域的先前相关研究进行了综述。