# Multi-Task Aspect-Based Sentiment: A Hybrid Sampling and Stance Detection Approach
###### Abstract
To overcome the problem, as mentioned earlier, self-training groups are looking for a robust way to develop aspect knowledge from the domain, such as choosing input based on confidence and creating automated pseudo-labels to reduce the effects of incomplete and unbalanced data [9]. By training a model with additional data that the model automatically labels, a model's prediction power can be improved. However, utilizing self-training always creates noisy pseudo-labels [10], which could lead to the issue of progressive drift. Re-weighting is a straightforward and common approach to solving this issue. Some self-training techniques used utility thresholds and uncertainty to assess the data quality of data that had been pseudo-labeled before giving low-quality pseudo-labels lower weights [11]. Assigning weights is more difficult in the following ways when using a self-learning method in an MTL and sequence-labeling MABSA. First, smaller weights are required for the generated pseudo-labeled data to reduce the effects of noise. Second, minority-labeled classes are assigned higher weights to assist the model in learning from insufficient data [12]. Finally, distinct subtasks may require different weights to be coordinated thoroughly because they have varying convergence rates and degrees of priority (primary tasks vs. auxiliary tasks). Typical re-weighting studies probably concentrated on one of the aforementioned circumstances. They created a predefined function to assign weights to the data, which fixed the denoising or unbalanced distribution issues. It is challenging to manually create a predefined weighting function that satisfies all three objectives while also adapting well to changing conditions [13; 14]. To address the abovementioned issues, such as imbalanced and insufficient data and MABSA sub-problem weight reassigning, this study presents a Mix-weight with the Meta-based Self-training method (MMS). MMS utilizes self-training to collect additional data and calculate different weights for several jobs in various situations. The proposed MMS model contains three components: the Student Model, the Instructor Model, and the extra meta-weightier. The student model has a similar construction as the IM. For the student model to receive thorough and objective supervision during training, the meta weights are utilized to reassign weights to both the gold and false labels. In contrast, the IM only uses gold-labeled data to train it, avoiding the influence of false labels. The meta-weightier offers subtask-specific weights of hybrid labeled data and data with no labels for the student model. Moreover, this paper utilized a 3-step update technique based on meta-learning to train the student model of MMS and the meta-weightier simultaneously to ensure the meta-weightier can provide accurate weights. The meta-weightier can use the recent response from the student model to build weights for current inputs by retaining two sets of parameters. Since it has already been updated using a similar input, the meta-weightier provides the student model with a better convergence. In the end, the student model makes final predictions using the IM and meta-weights guidance. In summary, the primary contributions of this paper are as follows:  
* The proposed hybrid techniques SMOTE-OSS-CNN deal with solving issues when utilizing an imbalanced dataset. * This paper developed MMS, which combines a conventional instructor-student structure with a novel meta-weightier to achieve self-training. To reduce the effects of noise, coordinate sub-tasks, and balance class labels in MABSA, the meta-weightier can produce weights specific to each subtask. * To jointly update the meta-weightier and student models, this paper develops a three-step meta-training technique. By using the suggested technique, MMS will be able to use the student model's most recent feedback to guide MMS in the direction of a temperature gradient.