  Speech acts are a speakers actions when performing an utterance within a
conversation, such as asking, recommending, greeting, or thanking someone,
expressing a thought, or making a suggestion. Understanding speech acts helps
interpret the intended meaning and actions behind a speakers or writers words.
This paper proposes a Twitter dialectal Arabic speech act classification
approach based on a transformer deep learning neural network. Twitter and
social media, are becoming more and more integrated into daily life. As a
result, they have evolved into a vital source of information that represents
the views and attitudes of their users. We proposed a BERT based weighted
ensemble learning approach to integrate the advantages of various BERT models
in dialectal Arabic speech acts classification. We compared the proposed model
against several variants of Arabic BERT models and sequence-based models. We
developed a dialectal Arabic tweet act dataset by annotating a subset of a
large existing Arabic sentiment analysis dataset (ASAD) based on six speech act
categories. We also evaluated the models on a previously developed Arabic Tweet
Act dataset (ArSAS). To overcome the class imbalance issue commonly observed in
speech act problems, a transformer-based data augmentation model was
implemented to generate an equal proportion of speech act categories. The
results show that the best BERT model is araBERTv2-Twitter models with a
macro-averaged F1 score and an accuracy of 0.73 and 0.84, respectively. The
performance improved using a BERT-based ensemble method with a 0.74 and 0.85
averaged F1 score and accuracy on our dataset, respectively.
