162 citations · 395 across the 4 of their papers we have counts for
6 papers
Mitigating Unwanted Biases with Adversarial Learning
Brian Hu Zhang, Blake Lemoine, Margaret Mitchell
Machine learning is a tool for building models that accurately represent input training data. When undesired biases concerning demographic groups are in the training data, well-tra…
Multi-Task Learning for Mental Health using Social Media Text
Adrian Benton, Margaret Mitchell, Dirk Hovy
We introduce initial groundwork for estimating suicide risk and mental health in a deep learning framework. By modeling multiple conditions, the system learns to make predictions a…
Visual Storytelling
Ting-Hao, Huang, Francis Ferraro +13
We introduce the first dataset for sequential vision-to-language, and explore how this data may be used for the task of visual storytelling. The first release of this dataset, SIND…
deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets
Michel Galley, Chris Brockett, Alessandro Sordoni +6
We introduce Discriminative BLEU (deltaBLEU), a novel metric for intrinsic evaluation of generated text in tasks that admit a diverse range of possible outputs. Reference strings a…
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
Alessandro Sordoni, Michel Galley, Michael Auli +6
We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations. A neural network architecture is used to add…
Exploring Nearest Neighbor Approaches for Image Captioning
Jacob Devlin, Saurabh Gupta, Ross Girshick +2
We explore a variety of nearest neighbor baseline approaches for image captioning. These approaches find a set of nearest neighbor images in the training set from which a caption m…