## 1 Introduction  
Spoken languages are a tool that is used by human beings to express their thoughts. Language plays an essential role in the faster growth of society. Language is utilized in society as a means for the interchange of ideas, as well as the sharing of information and experiences. The entire world is full of different communities, and each community has its independent language. Each language has its syntax and semantics. There is a great diversity of languages that makes communication between different communities difficult. Therefore, to enable communication among the communities, translation is required. Text is converted from one language to another through the process of translation. [PERSON] coined the term \"machine translation\" in his 1949 Memorandum on Translation, however, [PERSON] first used the term \"universal language\" in 1629, where one symbol may be used by numerous languages [1]. Despite decades of progress in this field, the most promising translation work only started in the early 1990s as a result of revolutionary work in artificial intelligence and computational linguistics. Third-generation machine translation systems or corpus-based architectures such as statistical and example-based techniques were developed through research in the 1990s. The example-based MT system uses combinations of pre-translated data examples from its database [2]. Since 1990, a number of research groups at UMIST, the University of Brussels, Grenoble University, and the Science University of Malaysia have been conducting experiments with \"dialogue-based MT\" systems. In these systems, the text that is going to be translated is composed or written through a collaborative process involving both a human and a machine. A brand-new end-to-end encoder-decoder framework for machine translation was put forth in 2013 [3]. Their work might be seen as the inception of neural machine translation (NMT), a technique that uses deep learning neural networks to map between different natural languages. A linked system of nodes that is partially based on the human brain is called a neural network. These nodes are part of an information system that processes incoming data to generate output. A sequence-to-sequence neural network (Seq2 Seq) is a type of neural network that analyses a source-language sentence and generates a target-language sentence in response [4; 5]. Identical to how the human brain functions, neural translation systems are always hunting for the proper patterns and making independent decisions [6]. A deep learning-based neural network (NN) has two or more hidden layers in the network [7; 8]. NMT employs the softmax process on the output layer of a recurrent neural network. The computational complexity of the softmax function is exponentially large, due to which the NMT system has a limited vocabulary. In the NMT framework, this triggers the issue of out-of-vocabulary (OOV) words. The definition of multi-word expressions (MWEs) is \"lexical items that: (a) can be broken down into multiple lexemes; and (b) show lexical, syntactic, semantic, pragmatic and/or statistical idiomaticity\" [9]. NMT can fail to learn and remember and replicate the MWEs since, in a high-dimensional vector, it represents the entire sentence. The objectives of this paper were to examine how to deal with OOV words and MWEs in the NMT system. To achieve this objective, a baseline NMT system was trained. To handle the OOV words, byte pair encoding (BPE) and word embedding (WE) were used while training the models in the NMT. The following is a list of the research questions that this article will address:  
1. How can we create a parallel corpus for the development of the MT system? 2. What are the various techniques that may be used to remove the noisy sentences in a parallel corpus? 3. What are the various pre-processing processes that must be completed before the dataset can be used to train an NMT system? 4. How should OOV terms be handled inside the NMT system? 5.