Aspect-based sentiment analysis (ABSA) task consists of three typical
subtasks: aspect term extraction, opinion term extraction, and sentiment
polarity classification. These three subtasks are usually performed jointly to
save resources and reduce the error propagation in the pipeline. However, most
of the existing joint models only focus on the benefits of encoder sharing
between subtasks but ignore the difference. Therefore, we propose a joint ABSA
model, which not only enjoys the benefits of encoder sharing but also focuses
on the difference to improve the effectiveness of the model. In detail, we
introduce a dual-encoder design, in which a pair encoder especially focuses on
candidate aspect-opinion pair classification, and the original encoder keeps
attention on sequence labeling. Empirical results show that our proposed model
shows robustness and significantly outperforms the previous state-of-the-art on
four benchmark datasets.

本研究在进行方面基情感分析 (ABSA) 任务时，提出了一种双编码器的设计方法，既保留了编码器共享的优点，又针对不同的任务进行了差异化改进，经实验证明，该模型在四个基准数据集上表现显著优于之前最先进的模型。

寻求共性但又有区别的差异：联合基于方面的情感分析模型

Seeking Common but Distinguishing Difference, A Joint Aspect-based  Sentiment Analysis Model

Non-Autoregressive machine Translation (NAT) models have demonstrated
significant inference speedup but suffer from inferior translation accuracy.
The common practice to tackle the problem is transferring the Autoregressive
machine Translation (AT) knowledge to NAT models, e.g., with knowledge
distillation. In this work, we hypothesize and empirically verify that AT and
NAT encoders capture different linguistic properties of source sentences.
Therefore, we propose to adopt Multi-Task learning to transfer the AT knowledge
to NAT models through encoder sharing. Specifically, we take the AT model as an
auxiliary task to enhance NAT model performance. Experimental results on WMT14
English-German and WMT16 English-Romanian datasets show that the proposed
Multi-Task NAT achieves significant improvements over the baseline NAT models.
Furthermore, the performance on large-scale WMT19 and WMT20 English-German
datasets confirm the consistency of our proposed method. In addition,
experimental results demonstrate that our Multi-Task NAT is complementary to
knowledge distillation, the standard knowledge transfer method for NAT.

通过多任务学习和编码器共享，将自回归机器翻译的知识转移到非自回归机器翻译模型中，实现翻译质量的提升。