Enhancing Performance and Stability of MAML for Few-Shot Sentiment Analysis: The Role of Domain Homogeneity and Learning Rate Annealing

Data annotation is a time-consuming and labor-intensive process in classification tasks. Recently, numerous
studies have explored the few-shot learning approach using meta-learning, particularly the MAML
algorithm. Most research aimed at improving MAML has focused on image classification rather than
text data, and the proposed enhancements often involve complex models that require significant processing
resources. Furthermore, there is a notable scarcity of research attempting to apply few-shot
learning methodologies to the Arabic language. This research paper aims to enhance the performance
of the Model-Agnostic Meta-Learning (MAML) algorithm in the domain of few-shot sentiment analysis,
with a specific focus on the Arabic language, which suffers from resource scarcity and a lack of multi-
domain labeled datasets. This paper addresses two primary challenges: the instability of the MAML
algorithm during training, and the importance of measuring divergence between training domains. To
improve training stability without requiring substantial processing resources, we propose using Cosine
Annealing to schedule the learning rate in the outer loop of MAML. Additionally, we present a significant
empirical finding demonstrating that the homogeneity of training domains has a substantial
impact on MAML performance. The validity of these contributions is verified through extensive experiments
on sentiment analysis datasets in both English and Arabic, including the Amazon Reviews
dataset and a multi-domain Arabic dataset compiled from several other research studies, processed,
and formatted to be suitable for the MAML algorithm. The results demonstrate the effectiveness of
the proposed method in improving the stability and performance of MAML and underscore the importance
of training domain homogeneity in few-shot learning scenarios with low processing resources.
Keywords: Meta Learning, Sentiment Analysis, Few-Shot Learning, Multi Domain Learning, Domain Homogeneity,
Cosine Annealing.