Designing energy-efficient buildings is an essential necessity since buildings are responsible for a significant proportion of energy consumption globally.
The shortcomings of NLG encoder-decoder models are primarily due to the lack of Arabic datasets suitable to train NLG models such as conversational agents.
Advances in English language representation enabled a more sample-efficient pre-training task by Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA).
In this paper, we develop the first advanced Arabic language generation model, AraGPT2, trained from scratch on a large Arabic corpus of internet text and news articles.
The shared task on Offensive Language Detection at the OSACT4 has aimed at achieving state of art profane language detection methods for Arabic social media.
Recently, with the surge of transformers based models, language-specific BERT based models have proven to be very efficient at language understanding, provided they are pre-trained on a very large corpus.
Ranked #1 on Sentiment Analysis on AJGT
As such, texts written in Arabizi are often disregarded in sentiment analysis tasks for Arabic.
Experiment results show that the developed hULMonA and multi-lingual ULM are able to generalize well to multiple Arabic data sets and achieve new state of the art results in Arabic Sentiment Analysis for some of the tested sets.
While transfer learning for text has been very active in the English language, progress in Arabic has been slow, including the use of Domain Adaptation (DA).
Our system for open domain question answering in Arabic (SOQAL) is based on two components: (1) a document retriever using a hierarchical TF-IDF approach and (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT.
Sentiment analysis is a highly subjective and challenging task.
Moreover, several features were evaluated along with different classification and regression techniques.
We also evaluate EmoWordNet in an emotion recognition task using SemEval 2007 news headlines dataset and we achieve an improvement compared to the use of DepecheMood.
While sentiment analysis in English has achieved significant progress, it remains a challenging task in Arabic given the rich morphology of the language.
Opinion mining in Arabic is a challenging task given the rich morphology of the language.
In this paper, we perform an extensive analysis on credibility of Arabic content on Twitter.
We focus on Arabic due to the recent popularity of blogs and microblogs in the Arab World and due to the lack of any such public corpora in Arabic.