Aspect-based sentiment analysis (ABSA) is a widely studied topic, most often
trained through supervision from human annotations of opinionated texts. These
fine-grained annotations include identifying aspects towards which a user
expresses their sentiment, and their associated polarities (aspect-based
sentiments). Such fine-grained annotations can be expensive and often
infeasible to obtain in real-world settings. There is, however, an abundance of
scenarios where user-generated text contains an overall sentiment, such as a
rating of 1-5 in user reviews or user-generated feedback, which may be
leveraged for this task. In this paper, we propose a VAE-based topic modeling
approach that performs ABSA using document-level supervision and without
requiring fine-grained labels for either aspects or sentiments. Our approach
allows for the detection of multiple aspects in a document, thereby allowing
for the possibility of reasoning about how sentiment expressed through multiple
aspects comes together to form an observable overall document-level sentiment.
We demonstrate results on two benchmark datasets from two different domains,
significantly outperforming a state-of-the-art baseline.

基于 VAE 的主题建模方法，利用文档级别的监督，在不需要精细标注方面或情感方面的标签的情况下完成 ABSA，能够检测文档中的多个方面，从而合理推测多个方面的情感是如何融合形成可观察的整体文档级情感的。通过对两个不同领域的基准数据集的实验结果证明，该方法显著优于最先进的基准模型。

无细粒度标签的多方面情感分析的文档级监督

Document-Level Supervision for Multi-Aspect Sentiment Analysis Without  Fine-grained Labels

We introduce HTLM, a hyper-text language model trained on a large-scale web
crawl. Modeling hyper-text has a number of advantages: (1) it is easily
gathered at scale, (2) it provides rich document-level and end-task-adjacent
supervision (e.g. class and id attributes often encode document category
information), and (3) it allows for new structured prompting that follows the
established semantics of HTML (e.g. to do zero-shot summarization by infilling
title tags for a webpage that contains the input text). We show that
pretraining with a BART-style denoising loss directly on simplified HTML
provides highly effective transfer for a wide range of end tasks and
supervision levels. HTLM matches or exceeds the performance of comparably sized
text-only LMs for zero-shot prompting and fine-tuning for classification
benchmarks, while also setting new state-of-the-art performance levels for
zero-shot summarization. We also find that hyper-text prompts provide more
value to HTLM, in terms of data efficiency, than plain text prompts do for
existing LMs, and that HTLM is highly effective at auto-prompting itself, by
simply generating the most likely hyper-text formatting for any available
training data. We will release all code and models to support future HTLM
research.

介绍了一种基于 HTML 训练的超文本语言模型 HTLM，使用 BART-style denoising loss 在简化 HTML 上进行预训练可对广泛端任务和监督级别提供高效的迁移，同时比纯文本模型表现更好，能够自动完成大部分的超文本格式转换，且零样本摘要效果最佳。