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This paper shows how to use gaze measurements obtained via eye-tracking to improve models for deletion-based sentence compression. It uses information from different data sources, eliminating the need to reply on data that is doubly annotated with sentence-compression and eye-tracking information, and points to data utilization methodology that could lead to larger improvements in the future.
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This paper addresses a novel problem of modeling interpersonal states between characters and how they evolve overtime. It brings new insights into unsupervised relationship modeling which involves joint learning a set of relationship descriptors and their trajectories. It was selected to receive a best paper award for its originality.
Learning to Compose Neural Networks for Question Answering
Jacob Andreas, Marcus Rohrbach, Trevor Darrell and Dan Klein
This paper is noteworthy for the innovative algorithmic ideas which bring together the strengths of neural network models and compositional logical semantics, and it demonstrates this approach in two different experiments, text-based question answering as well as image-based question answering, showing impact in multiple communities. The paper is pointing in a good direction for people to continue to expand, challenge and enhance this work.