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CLIP

 

Commonsense Intelligence: Cracking the Longstanding Challenge in AI

 

Causal machine learning and challenges in decision-making with real-world data

 

Defeasible Inference in Natural Language

 

Challenges and Opportunities in Evaluating Progress in NLP

Abstract: The past few years have seen remarkable advances in NLP, as evidenced both by continued and rapid gains on benchmark tasks, as well as by the increasing prominence of real NLP systems in the wild.  In assessing such progress, however, it is important to ask not only what system achieves the best performance, but how it achieves that level of performance, how much we can trust the evaluation, and what the consequences of deploying such a system might be.

 

A Typology of Ethical Risks in Language Technology with an Eye Towards Where Transparent Documentation Can Help

 

A Prioritization Model for Suicidality Risk Assessment

 

Structuring Posterior Inference: Unsupervised methods to find gendered words and generate sentiment lexica

 

Meta-Learning for Few-Shot NMT Adaptation

Learning to summarize medical evidence

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