In this work, we survey a breadth of literature that has revealed the limitations of
predominant practices for dataset collection and use in the field of machine learning. We …

Detecting online toxicity has always been a challenge due to its inherent subjectivity. Factors
such as the context, geography, socio-political climate, and background of the producers …

Fact-checking has become increasingly important due to the speed with which both
information and misinformation can spread in the modern media ecosystem. Therefore …

The paper surveys evaluation methods of natural language generation (NLG) systems that
have been developed in the last few years. We group NLG evaluation methods into three …

Abstract Machine translation (MT) technology has facilitated our daily tasks by providing
accessible shortcuts for gathering, processing, and communicating information. However, it …

State-of-the-art models in NLP are now predominantly based on deep neural networks that
are opaque in terms of how they come to make predictions. This limitation has increased …

We introduce scientific claim verification, a new task to select abstracts from the research
literature containing evidence that SUPPORTS or REFUTES a given scientific claim, and to …

Disparate biases associated with datasets and trained classifiers in hateful and abusive
content identification tasks have raised many concerns recently. Although the problem of …

Fact verification has attracted a lot of attention in the machine learning and natural language
processing communities, as it is one of the key methods for detecting misinformation …

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COMMUNICATIONS OF THE ACM | JANUARY 2024 | VOL. 67 | NO. 1 research IMA GE B Y …