The purpose of this work is to offer light on how ground-truth utterances may influence the evolution of speech systems in terms of naturalness, intelligibility, and understanding.
The increasing electricity demand and the need for clean and renewable energy resources to satisfy this demand in a cost-effective manner, imposes new challenges on researchers and developers to maximize the output of these renewable resources at all times.
The results showed that the use of multi-task learning and pre-trained word embeddings noticeably enhanced the quality of image captioning, however the presented results shows that Arabic captioning still lags behind when compared to the English language.
The role of predicting sarcasm in the text is known as automatic sarcasm detection.
LSTM and CNN networks were implemented using raw features: MFCC and MEL, where FCNN was explored on the pre-trained vectors while varying the hyper-parameters of these networks to obtain the best results for each dataset and task.
Our winning solution itself came in the form of an ensemble of different training iterations of our pre-trained BERT model, which achieved a micro-averaged F1-score of 26. 78% on the subtask at hand.