8 papers
When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer Learning
Orion Weller, Kevin Seppi, Matt Gardner
Transfer learning (TL) in natural language processing (NLP) has seen a surge of interest in recent years, as pre-trained models have shown an impressive ability to transfer to nove…
Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification
Wilson Fearn, Orion Weller, Kevin Seppi
Text classification is a significant branch of natural language processing, and has many applications including document classification and sentiment analysis. Unsurprisingly, thos…
Humor Detection: A Transformer Gets the Last Laugh
Orion Weller, Kevin Seppi
Much previous work has been done in attempting to identify humor in text. In this paper we extend that capability by proposing a new task: assessing whether or not a joke is humoro…
Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models
Varun Kumar, Alison Smith-Renner, Leah Findlater +2
To address the lack of comparative evaluation of Human-in-the-Loop Topic Modeling (HLTM) systems, we implement and evaluate three contrasting HLTM modeling approaches using simulat…
Automatic Evaluation of Local Topic Quality
Jeffrey Lund, Piper Armstrong, Wilson Fearn +4
Topic models are typically evaluated with respect to the global topic distributions that they generate, using metrics such as coherence, but without regard to local (token-level) t…
Cross-referencing using Fine-grained Topic Modeling
Jeffrey Lund, Piper Armstrong, Wilson Fearn +3
Cross-referencing, which links passages of text to other related passages, can be a valuable study aid for facilitating comprehension of a text. However, cross-referencing requires…