# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 6 What Are the Available Datasets Used for Short-text Semantic Similarity?
Secondly, reference answers have more than one correct answer, and there is no golden standard way to grade and score such answers [133; 141]. Suggested Improvements: The mBERT model has the ability to understand multilingual datasets. [PERSON], L et al. [142] introduced a multi-class Urdu dataset for sentiment analysis. They further fine-tuned the mBERT model and identified the positive, negative, and neutral reviews. Secondly, scoring rubrics and tools such as c-rater can be used to score and grade the answers. SQAS Challenge 05: Every question has a different effect on the intent type of answers, including questions such as list type, descriptive, factoid, open-ended, and so on. Such types of questions require answers in different forms. List-type questions require one or more than two words to answer the questions. However, essay-type answers usually come from descriptive-type questions. Factoid questions are based on factual answers, such as \"what is the capital city of Pakistan?\", and the answer can be a single statement or one word--'Islamabad\". Moreover, open-ended questions expect answers in open thoughts [19]. There is no specific answer limit for such types of questions, which makes it more challenging for the deep learning models to identify the correct or relevant answers [29; 82]. Suggested Improvements: Deep learning models are able to adjust the length of sentences, and capture the weights accordingly. The variants of the BERT (SBERT, BioBERT, Roberta, KeyBERT) models are suitable for short-length sentences. However, these models use default vocabulary and workpiece tokenizers for language understanding. Other models, such as CNN, RNN, and Bi-LSTM, also use various preprocessing techniques to limit and normalize the sentences [16; 20; 46; 49]. SQAS challenge 06: In a question-answering systems, there is still room to explore the specific length of a short sentence. Some studies use 10-20 words [143], some studies recommend less than 20 words [144], and some recommend less than 15 words [132]. Hence, there is no set of standards that explains the length of short sentences for machine learning algorithms. Suggested Improvements: Computational linguistics can be used in order to identify the length of a short sentence. SQAS Challenge 07: Various methods perform poorly on a number of QA datasets, as the datasets are not cleaned up. Moreover, applying the aligning techniques to answers can remove various relevant words, which can affect the sentence embeddings. [36; 145]. Suggested Improvements: Various techniques, such as the universal sentence encoder [146], can be used to improve sentence-level embeddings. Furthermore, preprocessing techniques can be utilized to clean the dataset. SQAS Challenge 08: The number of studies working on two-way, three-way, and five-way answer categories. Many machine learning algorithms still have difficulty dealing with answers that are semantically ambiguous [10; 147] and answers that match the main reference answer in multiple senses. Suggested improvements: [PERSON] et al. [67] proposed HowNet with a knowledge-enhanced graph transformer to deal with ambiguity and create multiple senses from sentences. Other deep learning models, such as the BERT, ELMO, and GPT-2, can sort the polysemy and ambiguity from natural language responses.