Aspect-based Sentiment Analysis (ABSA) is a type of fine-grained sentiment
analysis (SA) that identifies aspects and the associated opinions from a given
text. In the digital era, ABSA gained increasing popularity and applications in
mining opinionated text data to obtain insights and support decisions. ABSA
research employs linguistic, statistical, and machine-learning approaches and
utilises resources such as labelled datasets, aspect and sentiment lexicons and
ontology. By its nature, ABSA is domain-dependent and can be sensitive to the
impact of misalignment between the resource and application domains. However,
to our knowledge, this topic has not been explored by the existing ABSA
literature reviews. In this paper, we present a Systematic Literature Review
(SLR) of ABSA studies with a focus on the research application domain, dataset
domain, and the research methods to examine their relationships and identify
trends over time. Our results suggest a number of potential systemic issues in
the ABSA research literature, including the predominance of the
``product/service review'' dataset domain among the majority of studies that
did not have a specific research application domain, coupled with the
prevalence of dataset-reliant methods such as supervised machine learning. This
review makes a number of unique contributions to the ABSA research field: 1) To
our knowledge, it is the first SLR that links the research domain, dataset
domain, and research method through a systematic perspective; 2) it is one of
the largest scoped SLR on ABSA, with 519 eligible studies filtered from 4191
search results without time constraint; and 3) our review methodology adopted
an innovative automatic filtering process based on PDF-mining, which enhanced
screening quality and reliability. Suggestions and our review limitations are
also discussed.

该论文通过系统文献综述方法对 Aspect-based Sentiment Analysis (ABSA) 研究进行了研究领域、数据集领域和研究方法的分析，揭示了 ABSA 研究文献中存在的一些系统性问题，并提出了一些建议。

基于方面的情感分析（ABSA）的系统综述：领域、方法与趋势

A Systematic Review of Aspect-based Sentiment Analysis (ABSA): Domains,  Methods, and Trends

While coreference resolution is defined independently of dataset domain, most
models for performing coreference resolution do not transfer well to unseen
domains. We consolidate a set of 8 coreference resolution datasets targeting
different domains to evaluate the off-the-shelf performance of models. We then
mix three datasets for training; even though their domain, annotation
guidelines, and metadata differ, we propose a method for jointly training a
single model on this heterogeneous data mixture by using data augmentation to
account for annotation differences and sampling to balance the data quantities.
We find that in a zero-shot setting, models trained on a single dataset
transfer poorly while joint training yields improved overall performance,
leading to better generalization in coreference resolution models. This work
contributes a new benchmark for robust coreference resolution and multiple new
state-of-the-art results.

本研究提出了一种混合训练的方法，旨在解决针对不同领域的 8 个共指消解数据集在模型的零 - shot 迁移表现上存在的问题，并构建了一个新的数据集，取得了多个最先进的结果。

论指代消解中的泛化

On Generalization in Coreference Resolution

Numerous recent works have proposed pretraining generic visio-linguistic
representations and then finetuning them for downstream vision and language
tasks. While architecture and objective function design choices have received
attention, the choice of pretraining datasets has received little attention. In
this work, we question some of the default choices made in literature. For
instance, we systematically study how varying similarity between the
pretraining dataset domain (textual and visual) and the downstream domain
affects performance. Surprisingly, we show that automatically generated data in
a domain closer to the downstream task (e.g., VQA v2) is a better choice for
pretraining than "natural" data but of a slightly different domain (e.g.,
Conceptual Captions). On the other hand, some seemingly reasonable choices of
pretraining datasets were found to be entirely ineffective for some downstream
tasks. This suggests that despite the numerous recent efforts, vision &
language pretraining does not quite work "out of the box" yet. Overall, as a
by-product of our study, we find that simple design choices in pretraining can
help us achieve close to state-of-art results on downstream tasks without any
architectural changes.

该研究在研究预训练数据集的选择时发现，与下游任务相似的自动生成数据集是预训练的更好选择，而某些合理的预训练数据集对某些下游任务完全无效，这表明视听语言预训练仍需要简单的设计选择来实现接近最新成果的结果。