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20172021
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cs.CL2020

Generating Narrative Text in a Switching Dynamical System

Noah Weber, Leena Shekhar, Heeyoung Kwon +2

Early work on narrative modeling used explicit plans and goals to generate stories, but the language generation itself was restricted and inflexible. Modern methods use language mo…

cs.CL2020

Causal Inference of Script Knowledge

Noah Weber, Rachel Rudinger, Benjamin Van Durme

When does a sequence of events define an everyday scenario and how can this knowledge be induced from text? Prior works in inducing such scripts have relied on, in one form or anot…

cs.CL2018

Hierarchical Quantized Representations for Script Generation

Noah Weber, Leena Shekhar, Niranjan Balasubramanian +1

Scripts define knowledge about how everyday scenarios (such as going to a restaurant) are expected to unfold. One of the challenges to learning scripts is the hierarchical nature o…

cs.CL2018

The Fine Line between Linguistic Generalization and Failure in Seq2Seq-Attention Models

Noah Weber, Leena Shekhar, Niranjan Balasubramanian

Seq2Seq based neural architectures have become the go-to architecture to apply to sequence to sequence language tasks. Despite their excellent performance on these tasks, recent wo…

cs.CL2018

Controlling Decoding for More Abstractive Summaries with Copy-Based Networks

Noah Weber, Leena Shekhar, Niranjan Balasubramanian +1

Attention-based neural abstractive summarization systems equipped with copy mechanisms have shown promising results. Despite this success, it has been noticed that such a system ge…

cs.CL2017

Event Representations with Tensor-based Compositions

Noah Weber, Niranjan Balasubramanian, Nathanael Chambers

Robust and flexible event representations are important to many core areas in language understanding. Scripts were proposed early on as a way of representing sequences of events fo…