87 citations · 140 across the 15 of their papers we have counts for
9 papers · 1 filter
Clustering-Oriented Representation Learning with Attractive-Repulsive Loss
Kian Kenyon-Dean, Andre Cianflone, Lucas Page-Caccia +3
The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the training data; building useful representatio…
Multi-task Learning over Graph Structures
Pengfei Liu, Jie Fu, Yue Dong +2
We present two architectures for multi-task learning with neural sequence models. Our approach allows the relationships between different tasks to be learned dynamically, rather th…
Contextualized Non-local Neural Networks for Sequence Learning
Pengfei Liu, Shuaichen Chang, Xuanjing Huang +2
Recently, a large number of neural mechanisms and models have been proposed for sequence learning, of which self-attention, as exemplified by the Transformer model, and graph neura…
The Knowref Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution
Ali Emami, Paul Trichelair, Adam Trischler +3
We introduce a new benchmark for coreference resolution and NLI, Knowref, that targets common-sense understanding and world knowledge. Previous coreference resolution tasks can lar…
A Knowledge Hunting Framework for Common Sense Reasoning
Ali Emami, Noelia De La Cruz, Adam Trischler +2
We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms o…
BanditSum: Extractive Summarization as a Contextual Bandit
Yue Dong, Yikang Shen, Eric Crawford +2
In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels. We call…