Sentiment Analysis (SA) is an indispensable task for many real-world
applications. Compared to limited resourced languages (i.e., Arabic, Bengali),
most of the research on SA are conducted for high resourced languages (i.e.,
English, Chinese). Moreover, the reasons behind any prediction of the Arabic
sentiment analysis methods exploiting advanced artificial intelligence
(AI)-based approaches are like black-box - quite difficult to understand. This
paper proposes an explainable sentiment classification framework for the Arabic
language by introducing a noise layer on Bi-Directional Long Short-Term Memory
(BiLSTM) and Convolutional Neural Networks (CNN)-BiLSTM models that overcome
over-fitting problem. The proposed framework can explain specific predictions
by training a local surrogate explainable model to understand why a particular
sentiment (positive or negative) is being predicted. We carried out experiments
on public benchmark Arabic SA datasets. The results concluded that adding noise
layers improves the performance in sentiment analysis for the Arabic language
by reducing overfitting and our method outperformed some known state-of-the-art
methods. In addition, the introduced explainability with noise layer could make
the model more transparent and accountable and hence help adopting AI-enabled
system in practice.

本研究提出了一种可解释的情感分类框架，通过在双向长短期记忆（BiLSTM）和卷积神经网络（CNN）-BiLSTM 模型上引入噪声层，克服了过拟合问题，实验证明添加噪声层可以提高阿拉伯语情感分析的性能。

具噪深层可解释模型的阿拉伯情感分析

Arabic Sentiment Analysis with Noisy Deep Explainable Model

The availability of large labeled datasets has allowed Convolutional Network
models to achieve impressive recognition results. However, in many settings
manual annotation of the data is impractical; instead our data has noisy
labels, i.e. there is some freely available label for each image which may or
may not be accurate. In this paper, we explore the performance of
discriminatively-trained Convnets when trained on such noisy data. We introduce
an extra noise layer into the network which adapts the network outputs to match
the noisy label distribution. The parameters of this noise layer can be
estimated as part of the training process and involve simple modifications to
current training infrastructures for deep networks. We demonstrate the
approaches on several datasets, including large scale experiments on the
ImageNet classification benchmark.

本论文探究了在实际情况中，数据标注不可避免地有可能存在噪声标签的情况下，利用引入噪声层的方法，在 Convolutional Network 模型上进行判别性训练以获得更好的性能。同时在多个数据集上进行了实验，包括在 ImageNet 分类基准测试上的大规模试验。