(T)ACSA tasks, including aspect-category sentiment analysis (ACSA) and
targeted aspect-category sentiment analysis (TACSA), aims at identifying
sentiment polarity on predefined categories. Incremental learning on new
categories is necessary for (T)ACSA real applications. Though current
multi-task learning models achieve good performance in (T)ACSA tasks, they
suffer from catastrophic forgetting problems in (T)ACSA incremental learning
tasks. In this paper, to make multi-task learning feasible for incremental
learning, we proposed Category Name Embedding network (CNE-net). We set both
encoder and decoder shared among all categories to weaken the catastrophic
forgetting problem. Besides the origin input sentence, we applied another input
feature, i.e., category name, for task discrimination. Our model achieved
state-of-the-art on two (T)ACSA benchmark datasets. Furthermore, we proposed a
dataset for (T)ACSA incremental learning and achieved the best performance
compared with other strong baselines.

本文介绍了一种面向 (T) ACSA 任务的分类名称嵌入网络 (CNE-Net)，通过共享编码器和解码器等方式，使得多任务学习在增量学习上变得可行，同时提出了一种 (T) ACSA 增量学习数据集，与其他基线相比，实现了最佳性能表现。

一种具有类别名称嵌入的多任务增量学习框架，用于方面 - 类别情感分析

A Multi-Task Incremental Learning Framework with Category Name Embedding  for Aspect-Category Sentiment Analysis

Aspect based sentiment analysis (ABSA) can provide more detailed information
than general sentiment analysis, because it aims to predict the sentiment
polarities of the given aspects or entities in text. We summarize previous
approaches into two subtasks: aspect-category sentiment analysis (ACSA) and
aspect-term sentiment analysis (ATSA). Most previous approaches employ long
short-term memory and attention mechanisms to predict the sentiment polarity of
the concerned targets, which are often complicated and need more training time.
We propose a model based on convolutional neural networks and gating
mechanisms, which is more accurate and efficient. First, the novel Gated
Tanh-ReLU Units can selectively output the sentiment features according to the
given aspect or entity. The architecture is much simpler than attention layer
used in the existing models. Second, the computations of our model could be
easily parallelized during training, because convolutional layers do not have
time dependency as in LSTM layers, and gating units also work independently.
The experiments on SemEval datasets demonstrate the efficiency and
effectiveness of our models.

本文研究了利用卷积神经网络和门机制的模型，提高方面基情感分析的准确性和效率。该模型的提出主要在于采用了 Gated Tanh-ReLU Units 来根据给定的方面或实体选择性地输出情感特征。实验结果显示其在 SemEval 数据集上的性能优越。