Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Hassan, Sabit"'
Patients who effectively manage their symptoms often demonstrate higher levels of engagement in conversations and interventions with healthcare practitioners. This engagement is multifaceted, encompassing cognitive and socio-affective dimensions. Con
Externí odkaz:
http://arxiv.org/abs/2305.19981
Autor:
Hassan, Sabit, Alikhani, Malihe
Despite recent advancements, NLP models continue to be vulnerable to bias. This bias often originates from the uneven distribution of real-world data and can propagate through the annotation process. Escalated integration of these models in our lives
Externí odkaz:
http://arxiv.org/abs/2305.17013
Content moderation is the process of flagging content based on pre-defined platform rules. There has been a growing need for AI moderators to safeguard users as well as protect the mental health of human moderators from traumatic content. While prior
Externí odkaz:
http://arxiv.org/abs/2302.09618
Using style-transfer models to reduce offensiveness of social media comments can help foster a more inclusive environment. However, there are no sizable datasets that contain offensive texts and their inoffensive counterparts, and fine-tuning pretrai
Externí odkaz:
http://arxiv.org/abs/2209.08207
End-to-end sign language generation models do not accurately represent the prosody in sign language. A lack of temporal and spatial variations leads to poor-quality generated presentations that confuse human interpreters. In this paper, we aim to imp
Externí odkaz:
http://arxiv.org/abs/2203.09679
We introduce a generic, language-independent method to collect a large percentage of offensive and hate tweets regardless of their topics or genres. We harness the extralinguistic information embedded in the emojis to collect a large number of offens
Externí odkaz:
http://arxiv.org/abs/2201.06723
The emergence of the COVID-19 pandemic and the first global infodemic have changed our lives in many different ways. We relied on social media to get the latest information about the COVID-19 pandemic and at the same time to disseminate information.
Externí odkaz:
http://arxiv.org/abs/2201.06496
Emotion detection can provide us with a window into understanding human behavior. Due to the complex dynamics of human emotions, however, constructing annotated datasets to train automated models can be expensive. Thus, we explore the efficacy of cro
Externí odkaz:
http://arxiv.org/abs/2106.06017
Pretraining Bidirectional Encoder Representations from Transformers (BERT) for downstream NLP tasks is a non-trival task. We pretrained 5 BERT models that differ in the size of their training sets, mixture of formal and informal Arabic, and linguisti
Externí odkaz:
http://arxiv.org/abs/2102.10684
Autor:
Mubarak, Hamdy, Hassan, Sabit
Over the past few months, there were huge numbers of circulating tweets and discussions about Coronavirus (COVID-19) in the Arab region. It is important for policy makers and many people to identify types of shared tweets to better understand public
Externí odkaz:
http://arxiv.org/abs/2012.01462