## 2 Related Work  
Natural Language Processing (NLP) techniques can be applied in different ways and to different real-world and industrial cases. Many NLP techniques are also implemented in machine learning or deep learning applications to solve specific tasks in specific domains. In the healthcare system, NLP is used to automatically classify clinical reports [7] or to infer people's mental states from what they write on social media [8]; in the marketing and customer care sectors, several companies implement an NLP application to predict customer needs [9] or analyse customer reaction or satisfaction [10]. These models typically use reviews or social media posts and tweets to predict how users feel about a specific topic, and companies can use this information to improve their marketing strategies. A very popular and implemented NLP solution consists of conversational systems. In recent years, many companies have created their virtual assistant solutions to improve customer communication, provide better 24/7 support, and reduce the workload of employees [11; 12]. A conversational system is a complex structure that leverages on several NLP subsystems, such as a Natural Language Understanding model and a multi-label classifier that predicts the intent of the user. In addition to NLP components, the realisation of a complete chatbot requires other structures, such as a front-end interface and a back-end data layer [12]. Today, there are many different frameworks that provide this kind of service, such as Watson IBM, Google Dialogflow, Microsoft Luis, and Facebook Wit.ai [13]. In recent years, several approaches and frameworks have been developed to solve scoring and graduation tasks. The two main branches of this problem are: Automatic Essay Scoring (AES) [14] and Automatic Short Answer Grading (ASAG) [15]. This second task can be tackled in two ways: with a regression approach, which tries to predict a score in a continuous range (usually between 0 and 5), and a classification approach, in which the system has to assert the correctness of the user's answer. Following the evolution of machine learning and deep learning, over the years, many different techniques have been implemented to solve ASAG tasks. One of the first methods combines the Bag-of-Words representation and the Support Vector Machine to provide a score [16]. The words contained in the sentences were also used with overlapping algorithms to measure the similarity between the reference and the student's response [17]. Similarly, [PERSON] and [PERSON] [18] proposed an unsupervised Bag-of-Words approach that deals with students' responses using text-to-text similarity, in particular 13 string-based and 2 corpus-based similarity measures combined to compute the similarity. Another popular method that uses the words contained in the answers is presented in [19]; its authors propose a mixed unsupervised and supervised approach that combines a clustering algorithm based on the vocabulary used in the answers and a mathematically supervised model based on the Hamming distance between the reference and the student's answer. All the methods previously described rely on the classical Bag-of-Words NLP technique for representing sentences and documents; however, since the introduction of word embedding techniques [20], Bag-of-Words cannot be seen as the state-of-the-art anymore [21; 22]. Moreover, these methods are based on classical machine learning algorithms, while our work proposes a neural network-based approach which allows one to obtain much better performances for text classification [2; 7; 23] and similarity-based tasks [24; 25]. The introduction of word embedding methods such as GloVe [26] and Word2 Vec [20] raised the issues of computing a vectorial representation of sentences and how to measure the distance (in terms of their meaning) among them. This task is typically called _semantic similarity_, and the closer the vector representations, the more sentences have a similar meaning. These techniques have been exploited in ASAG by [PERSON] et al.