This thesis addresses some of the challenges of translating morphologically rich languages (MRLs). Words in MRLs have more complex structures than those in other languages, so that a word can be viewed as a hierarchical structure with several internal subunits. Accordingly, word-based models in which words are treated as atomic units are not suitable for this set of languages. As a commonly used and eff ective solution, morphological decomposition is applied to segment words into atomic and meaning-preserving units, but this raises other types of problems some of which we study here. We mainly use neural networks (NNs) to perform machine translation (MT) in our research and study their diff erent properties. However, our research is not limited to neural models alone as we also consider some of the difficulties of conventional MT methods. First we try to model morphologically complex words (MCWs) and provide better word-level representations. Words are symbolic concepts which are represented numerically in order to be used in NNs. Our first goal is to tackle this problem and find the best representation for MCWs. In the next step we focus on language modeling (LM) and work at the sentence level. We propose new morpheme-segmentation models by which we finetune existing LMs for MRLs. In this part of our research we try to find the most efficient neural language model for MRLs. After providing word- and sentence-level neural information in the first two steps, we try to use such information to enhance the translation quality in the statistical machine translation (SMT) pipeline using several diff erent models. Accordingly, the main goal in this part is to find methods by which deep neural networks (DNNs) can improve SMT. One of the main interests of the thesis is to study neural machine translation (NMT) engines from diff erent perspectives, and finetune them to work with MRLs. In the last step we target this problem and perform end-to-end sequence modeling via NN-based models. NMT engines have recently improved significantly and perform as well as state-of-the-art systems, but still have serious problems with morphologically complex constituents. This shortcoming of NMT is studied in two separate chapters in the thesis, where in one chapter we investigate the impact of diff erent non-linguistic morpheme-segmentation models on the NMT pipeline, and in the other one we benefit from a linguistically motivated morphological analyzer and propose a novel neural architecture particularly for translating from MRLs. Our overall goal for this part of the research is to find the most suitable neural architecture to translate MRLs. We evaluated our models on diff erent MRLs such as Czech, Farsi, German, Russian, and Turkish, and observed significant improvements. The main goal targeted in this research was to incorporate morphological information into MT and define architectures which are able to model the complex nature of MRLs. The results obtained from our experimental studies confirm that we were able to achieve our goal.