Aspect based sentiment analysis, predicting sentiment polarity of given
aspects, has drawn extensive attention. Previous attention-based models
emphasize using aspect semantics to help extract opinion features for
classification. However, these works are either not able to capture opinion
spans as a whole, or not able to capture variable-length opinion spans. In this
paper, we present a neat and effective structured attention model by
aggregating multiple linear-chain CRFs. Such a design allows the model to
extract aspect-specific opinion spans and then evaluate sentiment polarity by
exploiting the extracted opinion features. The experimental results on four
datasets demonstrate the effectiveness of the proposed model, and our analysis
demonstrates that our model can capture aspect-specific opinion spans.

本文提出了一种基于多个线性链 CRFs 的详细有效的结构化关注模型，通过聚合多个线性链 CRFs，该设计允许模型提取特定方面的意见跨度，然后利用提取的意见特征评估情感极性，实验证明该模型是有效的。

基于方面特定观点跨度的方面情感分析

Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans

Recent works have shown the benefit of integrating Conditional Random Fields
(CRFs) models into deep architectures for improving pixel-level prediction
tasks. Following this line of research, in this paper we introduce a novel
approach for monocular depth estimation. Similarly to previous works, our
method employs a continuous CRF to fuse multi-scale information derived from
different layers of a front-end Convolutional Neural Network (CNN). Differently
from past works, our approach benefits from a structured attention model which
automatically regulates the amount of information transferred between
corresponding features at different scales. Importantly, the proposed attention
model is seamlessly integrated into the CRF, allowing end-to-end training of
the entire architecture. Our extensive experimental evaluation demonstrates the
effectiveness of the proposed method which is competitive with previous methods
on the KITTI benchmark and outperforms the state of the art on the NYU Depth V2
dataset.

本文提出一种新的单目深度估计方法，使用带有结构化注意力模型的条件随机场来融合来自前端卷积神经网络不同层次的多尺度信息，并将其无缝集成到整个架构的端到端训练中， 在 KITTI 基准测试中排名竞争对手，在 NYU Depth V2 数据集上优于现有技术。