5 citations · 7 across the 2 of their papers we have counts for
5 papers
How Gender Debiasing Affects Internal Model Representations, and Why It Matters
Hadas Orgad, Seraphina Goldfarb-Tarrant, Yonatan Belinkov
Common studies of gender bias in NLP focus either on extrinsic bias measured by model performance on a downstream task or on intrinsic bias found in models' internal representation…
Intrinsic Bias Metrics Do Not Correlate with Application Bias
Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ricardo Muñoz Sanchez +2
Natural Language Processing (NLP) systems learn harmful societal biases that cause them to amplify inequality as they are deployed in more and more situations. To guide efforts at…
Scaling Systematic Literature Reviews with Machine Learning Pipelines
Seraphina Goldfarb-Tarrant, Alexander Robertson, Jasmina Lazic +3
Systematic reviews, which entail the extraction of data from large numbers of scientific documents, are an ideal avenue for the application of machine learning. They are vital to m…
Content Planning for Neural Story Generation with Aristotelian Rescoring
Seraphina Goldfarb-Tarrant, Tuhin Chakrabarty, Ralph Weischedel +1
Long-form narrative text generated from large language models manages a fluent impersonation of human writing, but only at the local sentence level, and lacks structure or global c…
Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation
Seraphina Goldfarb-Tarrant, Haining Feng, Nanyun Peng
Story composition is a challenging problem for machines and even for humans. We present a neural narrative generation system that interacts with humans to generate stories. Our sys…