# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 6 What Are the Available Datasets Used for Short-text Semantic Similarity?
For experimentation, the universal sentence encoder, Python 3.6, NLTK library, and GPU-2 have been widely used, and a number of studies used this benchmark for sentiment analysis, text classification, data augmentation, customer feedback systems, and information aggregation [127; 128; 129; 130]. SciEnsBank: In automated short-answer grading (ASAGs), the SciEnsBank is used as a corpus of questions and answers. This corpus lies under SemEval-2013 (Semantic evaluation) dataset and is known as a domain-specific corpus, which includes science-domain questions and answers [38]. The average sentence length is 15-20 words. Approx. 10,000 responses from 197 assignments in 15 different science fields are found in the SciEnsBanks [131]. The corpus further includes three categories to identify and grade the short answers (i) correct and incorrect (labeled two-way); (ii) correct, incorrect, and contradictory (labeled three-way); (iii) correct, partially correct, contradictory, irrelevant, and non-domain (labeled five-way). The corpus is suitable for multi-perspective evaluation [132]. Several studies import datasets using Google Colab, Python 3.6, and GPU TeslaK80 with 12 GB RAM. The SciEnsBank further includes unseen answers (UA), unseen questions (UQ), and an unseen domain (UD) [133; 134; 135; 136]. Moreover, Table 12 illustrates the summary of the datasets. What Are the Current Challenges and Suggested Improvements in Short Question-Answering Systems (SQAS)? This section discusses the challenges and suggested improvements in short question-answering systems (QAS). In order to do that, we reviewed approx. 25 studies. SQAS Challenge 01: In a short question-answering system, the student's answers are relatively very short, and most of the words in answers do not contribute rich semantics when mapped to the model answers. Secondly, the textual similarity is yet unable to perceive the syntactic semantics from student responses [1,4]. Consider the phrase \"develop a taste' and 'taste development'. The cosine value between phrases is 0.911, which is close to 1.000 [4]. Both of these phrases are incorrect from a grammatical standpoint, and a human judge can clearly tell the distinctions between them. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
Suggested Improvements: Language models such as the BERT, Roberta, ELMO, and GPT-2 understand sentences with extremely deep semantics [101]. The models extract the similarity based on context. Secondly, the language models use an attention mechanism. [PERSON] et al. [137] discovered evidence of grammatical representations from attention metrics. They further presented the syntactic subspaces that represent semantic information. SQAS Challenge 02: According to [PERSON] et al. [138], the majority of background knowledge and facts are ignored when matching the question-answer pairs. This is due to the fact that the question attributes are excluded from the knowledge base. Suggested Improvements: The self-attention mechanism can be used to understand context-dependent and context-independent information. Additionally, other kinds of extraneous knowledge, such as simple texts, can be useful for enhancing knowledge representations [138]. SQAS Challenge 03: In a question-answering systems, the majority of studies focused on grading the answers, providing the scoring rubric, and comparing the human-assigned score with the machine-provided score [83]. But more attention still has to be paid to separating the word embedding characteristics from student and reference answers based on their hyperparameters. Suggested Improvements. Window size, the location of the context window weightings, learning rate, activation rate, batch size, and dropout rate are examples of hyperparameters [76]. The best techniques for maximizing these parameters are Bayesian optimization, grid search, and random search [139; 140]. SQAS Challenge 04: Question-answering systems lack consistent support by using general domain question answers. Existing datasets are biased towards specific domains.