# Hybrid ANN-Based and Text Similarity Method for Automatic Short-Answer Grading in Polish
## 2 Related Work  
Automatic evaluation of short answers can be treated as a Short Text Similarity (STS) measurement. The similarity can be measured literally or meaningfully [7,14]. In [15], the following classes of STS methods were distinguished: string-based similarity, traditional models, vector space models, and the most promising--neural models. Various approaches for the automatic evaluation of short-answer tasks were proposed. One of them was the Grader Assistance System (GAS) [16]. It used DKPro Core 1.7.0 (read-to-use software for natural language processing) and DKPro Similarity libraries (framework for developing text similarity algorithms), and it computed lemmas using the Stanford lemmatizer component in DKPro Core and excluded stop words using [PERSON]'s German stop word list. Furthermore, a vector-based system was developed [17]. This system used different sentence representations, like word-vector representation, and similarity measures, like string similarity, knowledge-based similarity, and corpus-based similarity. Also, the Automatic Short-Answer Grading (ASAG) system was presented to explore the effectiveness of Machine Learning (ML) approaches, such as Naive Bayes (NB), Logistic Regression (LR), Decision Trees (DT), Support Vector Machine (SVM), and artificial neutral network (ANN), to compare them against Deep Belief Networks (DBN) and by extending the student answer to leveraging DBN with several extended features [18]. Moreover, another system was constructed that combines a response-based method, which extracts features from student answers, with a reference-based method that matches the student answers with target answers, and it outperformed a non-stacked combination and is helpful for most Natural Language Processing (NLP) cases [19]. Due to the various NLP issues and tasks, several researchers put significant effort into investigating new approaches and applications to solve NLP problems [20,21]. Moreover, the linguistic methods are finding more and more applications in various aspects, such as, for example, intelligent semantic analysis of medical data, emphasising automatic diagnostic feature description through image description languages and grammar formalism [22]. Nowadays, intelligent NLP methods increasingly use artificial intelligence. Some of them consider an artificial neural network as a significant solution, such as a system that combines Siamese bi-Long Short-Term Memory (bi-LSTMs), a novel pooling layer based on the Sinkhorn distance between LSTM state sequences, and a support vector ordinal output layer, and the system has scoring accuracy superior to recent baselines [23]. Another model was proposed for Automatic Text Scoring (ATS), which focused on extended word representation based on LSTM [24]. One more automatic scoring system is based on word embedding and paragraph embedding [25]. Existing attention networks use word-based or sentence-based attention, as in [26]. In [27], a novel and effective method was introduced: an attention-based deep learning method using lexicons and the Word2 vec model for word representation for sentiment analysis of social media text data. It combined the current state-of-the-art machine learning and NLP representation learning methods. As relational meaning is central to NLP, ref. [28] presented a new type of Relation Network (RN), a Semantic Feature-wise transformation Relation Network (SFRN), that learned relational information from QRA triples, a question (Q), reference answer (R), and student answer (A), for automatic short-answer grading, learning from two types of training data, using reference answers or rubrics. SFRN+ is the version with the Bidirectional Encoder Representations from Transformers (BERT) of Google company. In [29], the relationship between natural language processing and learning English as a Foreign Language (EFL) was explored. Natural language processing used in [30] showed the possibility of reducing the scoring burden and enabling more extensive studies.