With the widespread use of artificial intelligence (AI) systems and applications in our
everyday lives, accounting for fairness has gained significant importance in designing and …

Pretraining a language model (LM) on text has been shown to help various downstream
NLP tasks. Recent works show that a knowledge graph (KG) can complement text data …

Answering complex questions about textual narratives requires reasoning over both stated
context and the world knowledge that underlies it. However, pretrained language models …

Unwanted and often harmful social biases are becoming ever more salient in NLP research,
affecting both models and datasets. In this work, we ask whether training on …

Recent studies show that Natural Language Processing (NLP) technologies propagate
societal biases about demographic groups associated with attributes such as gender, race …

Evaluating the factual consistency of automatically generated summaries is essential for the
progress and adoption of reliable summarization systems. Despite recent advances, existing …

Implicit knowledge, such as common sense, is key to fluid human conversations. Current
neural response generation (RG) models are trained to generate responses directly …

Work on hate speech has made considering rude and harmful examples in scientific
publications inevitable. This situation raises various problems, such as whether or not to …

Warning: This paper contains content that is offensive and may be upsetting. Biased or toxic
speech can be harmful to various demographic groups. Therefore, it is not only important for …

Smooth and effective communication requires the ability to perform latent or explicit
commonsense inference. Prior commonsense reasoning benchmarks (such as SocialIQA …