## 5 Challenges  
One of the challenges facing technology developers is to develop a solution that understands natural language. This is because modern languages are long and complex, containing a large number of phrases that can mean different things depending on the region of the world in which they are used. One way of tackling the problem is through syntax, as it allows for the analysis of the combination of sentences that could have different meanings [8]. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Proposal** & **Extraction Approach** & **Model Scope** & **Datasets** & **Results F1 Score** \\ \hline Adverse Pregnancy Outcomes [98] & Lexically constrained sentence & Medical & Medication Abuse in Tweets & 0.9305 \\ and Potential COVID-19 & generation & & & \\ \hline ASK-RoBERTa [99] & Sentiment analysis & Lexical & Restaurant-14-16, Laptop-14 & 0.779, 0.821, 0.71, 0.792 \\ \hline RoBERTa-IAN [100] & Product reviews & Marketing & SEMVAL & 0.90 \\ \hline RoBERTa-CRF [101] & Toxic span detection & Sentiment analysis & Civil Comments & 0.66 \\ \hline Sarcastic RoBERTa [102] & Detect sarcasm in tweets & Sentiment analysis & Sarcasm & 0.526 \\ \hline RobeCzech [103] & Contextualised representation & Lexical & Czech Facebook dataset & 0.801 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Overview of RoBERTa Proposals. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Proposal** & **Extraction Approach** & **Model Scope** & **Model** & **Datasets** \\ \hline CodexDB [68] & Translate text & SQL-query processing & GPT-3 & WikiSQL, SPIDER \\ \hline GPT-3-ENS [70] & Medical dialogue summarisation & Medical & GPT-3 & Human labeled, GPT-3-ENS \\ \hline Cross-Domain Detection [71] & Cross-domain detection & Technical text & GPT-3 & SME-labeled, Proxy data \\ \hline Fake News Detection [72] & Detect fake news & News & XLNet & LIAR \\ \hline Show Me How To Revie [73] & Sentence generation & Lexical & XLNet & One-Billion-Word \\ \hline Low-resource neural [74] & Neural machine translation & Translation & XLNet & CCMT2019 \\ machine translation & & & & \\ \hline XLNet-GRU [75] & Detect fake news & News & XLNet-GRU & Cryptocurrency news \\ \hline Sentiment Analysis [76] & Tweet-based sentiment analysis & Medical & XLNet & COCO val \\ \hline AB-XLNet [107] & Drugs in discharge summaries & Medical & XLNet & \\ \hline ArxT5 [88] & Language generation & Lexical & T5 & Arabic MT datasets \\ \hline mTS [89] & Language generation & Lexical & T5 & \\ \hline Multimodal Emotion Recognition [90] & Emotion recognition & Sentiment analysis & T5 & IEMOCAP \\ \hline Extreme Multi-Domain [91] & Multi-task transfer learning & Lexical & T5 & \\ \hline Indet5 [92] & Natural language processing & Lexical & T5 & IndCorpus \\ \hline Ensemble-NQG-T5 [94] & Neural generation of questions & Questions and answers & T5 & SQuAD 2.0 \\ \hline Generated Questions for Thai [95] & Answer questions & Questions and answers & T5 & Wiki QA and iApp Wiki QA \\ \hline ER-Chat [96] & Chat room & Chat & T5 & \\ \hline Code-Related Tasks [93] & Correct code errors & Code-related & T5 & Bug-Fix Pairs \\ \hline Adverse Pregnancy Outcomes [98] & Lexical constrained sentence generation & Medical & RoBERTa & Medication Abuse in Tweets \\ and Potential COVID-19 & & & & \\ \hline ASK-RoBERTa [99] & Sentiment analysis & Lexical & RoBERTa & Restaurant-14, Restaurant-16, Laptop-14 \\ \hline RoBERTa-IAN [100] & Product reviews & Marketing & RoBERTa & SEMVAL \\ \hline RoBERTa-CRF [101] & Toxic span detection & Sentiment analysis & RoBERTa & Civil Comments \\ \hline Sarcastic RoBERTa [102] & Detect sarcasm in tweets & Sentiment analysis & RoBERTa & Sarcasm \\ \hline RobeCzech [103] & Contextualised representation & Lexical & RoBERTa & Czech Facebook dataset \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Overview of Natural Language Understanding Models Proposals. Solutions that use NLP models in their logic to understand language are growing, but as demand grows, so do the challenges of the technology.