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Learning from Task Descriptions
TLDR
We synthesize prior approaches to zero-shot learning in NLP and provide a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area. Expand
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Humor Detection: A Transformer Gets the Last Laugh
TLDR
We present a novel way of approaching this problem by building a model that learns to identify humorous jokes based on ratings gleaned from Reddit pages, consisting of almost 16,000 labeled instances. Expand
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The rJokes Dataset: a Large Scale Humor Collection
TLDR
Humor is a complicated language phenomenon that depends upon many factors, including topic, date, and recipient. Expand
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You Don't Have Time to Read This: An Exploration of Document Reading Time Prediction
TLDR
We perform a novel experiment to examine how different features of text contribute to the time it takes to read, distributing and collecting data from over a thousand participants. Expand
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Streaming Models for Joint Speech Recognition and Translation
TLDR
We develop an end-to-end streaming ST model based on a re-translation approach and compare against standard cascading approaches. Expand
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Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification
TLDR
We provide a comprehensive study that examines how preprocessing techniques affect the vocabulary size, model performance, and model run-time, evaluating ten techniques over four models and two datasets. Expand
Can Humor Prediction Datasets be used for Humor Generation? Humorous Headline Generation via Style Transfer
TLDR
We explore whether the format of popular humor prediction datasets can be used to successfully generate humorous text. Expand