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Learning human languages is a difficult task for a computer. However, Deep Learning (DL) techniques have enhanced performance significantly for almost all-natural language processing (NLP) tasks. Unfortunately, these models cannot be generalized for all the NLP tasks with similar performance. NLU (Natural Language Understanding) is a subset of NLP including tasks, like machine translation, dialogue-based systems, natural language inference, text entailment, sentiment analysis, etc. The advancement in the field of NLU is the collective performance enhancement in all these tasks. Even though MTL (Multi-task Learning) was introduced before Deep Learning, it has gained significant attention in the past years. This paper aims to identify, investigate, and analyze various language models used in NLU and NLP to find directions for future research. The Systematic Literature Review (SLR) is prepared using the literature search guidelines proposed by Kitchenham and Charters on various language models between 2011 and 2021. This SLR points out that the unsupervised learning method-based language models show potential performance improvement. However, they face the challenge of designing the general-purpose framework for the language model, which will improve the performance of multi-task NLU and the generalized representation of knowledge. Combining these approaches may result in a more efficient and robust multi-task NLU. This SLR proposes building steps for a conceptual framework to achieve goals of enhancing the performance of language models in the field of NLU.

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Framework for Deep Learning-Based Language Models Using Multi-Task Learning in Natural Language Understanding: A Systematic Literature Review and Future Directions