## 1 Introduction  
Machine translation (MT) applications have nowadays infiltrated almost every aspect of everyday activities. For the development of efficient MT solutions, reliable automated evaluation schemata are required. Over the past few years, neural network (NN) models have improved the state-of-the-art of different natural language processing (NLP) applications [1], such as language modeling [2, 3], improving answer ranking in community question answering [4], improving translation modeling [5, 6, 7], as well as evaluating machine translation output [4, 8, 9]. Embeddings are a powerful way of representing text, provided that they are able to capture the linguistic identity (morphosyntactic and semantic profile) of a sentence/word. In 2013, [PERSON] et al. [3] released the word2 vec library. Word2 vec became quickly the dominant approach for vectorizing textual data. The NLP models that were already well studied based on traditional approaches, such as latent semantic indexing (LSI) and vector representations using term frequency-inverse document frequency (TF-IDF) weighting, have been tested against word embeddings and, in most cases, word embeddings have come out on top. Since then, the research focus has shifted towards embedding approaches.  
The present study aims to find out how embeddings, obtained through various means, in combination with different kinds of information fuse, affect classification accuracy small and noisy dataset, when used to train a model to choose the best translation output. The target languages (in contrast to the source language) are rich in morphology, as the proposed schema is applied to the English-Greek (EN-EL) and English-Italian (EN-IT) language pairs. Greek and Italian languages have a rich inflectional morphology, as the nouns have different grammatical morphemes for the genders and the verbs have different grammatical morphemes for the two numbers and for the first, second and third person as well. In particular, the proposed NN learning schema is set up to test:  
* two different forms of text structure (an informal (noisy) corpus (\(C1\)), and a formal, well-structured corpus (\(C2\))) will be experimented with;
* a comparative analysis of two different ways of calculating embeddings (the straightforwardly mathematically calculated layer embeddings and the use of pre-trained embeddings) will be conducted
* the application of the SMOTE [10] oversampling technique during training will be investigated in order to overcome data imbalance phenomena;
* the use of two string-based linguistic features (hand-crafted), that capture the similarity between the MT outputs and the reference translation (\(Sr\)).  
Further innovative aspects of the present work include:  
* a novel deep learning architecture with innovative feeding structure that involves features of various linguistic levels and sources;
* a qualitative linguistic analysis that aims to reveal linguistic phenomena linked to poor/rich morphology, that impact on translation performance;
* the exploration of two different validation options (k-fold cross validation (\(CV\)) and Percentage split);
* the application of feature selection and dimensionality reduction methods;
* the application of the proposed multi-input, multi-level learning schema on text data from very different genres.  
The rest of the paper is organized as follows--Section 2 presents the related work in the addressed scientific area. Section 3 describes the data sets (corpora), the feature set used, the learning framework and the network settings. Section 4 describes more experimental details and the results of the classification process. Finally, Section 5 presents the paper's conclusions and directions for future research.