Amanda Bertsch

I am a first-year masters student in the Language Technologies Institute at Carnegie Mellon University, advised by Matt Gormley.

My research interest is in conditional text generation, especially for summarization. I’m interested in building more interpretable, faithful models that are robust to non-standard (e.g. informal or domain-specific) language. Currently, my work is in dialogue summarization.

I received my bachelors in math and computer science from the University of Arizona, where I was advised by Steven Bethard.

In my spare time, I write and read speculative fiction and play tabletop games.

news

Nov 11, 2021	I presented my undergraduate thesis work on bias detection at the 2021 Workshop on Noisy User-generated Text!

selected publications

ACL

Detection of Puffery on the English Wikipedia

Bertsch, Amanda, and Bethard, Steven

In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021) Nov 2021

Abs PDF

On Wikipedia, an online crowdsourced encyclopedia, volunteers enforce the encyclopedia’s editorial policies. Wikipedia’s policy on maintaining a neutral point of view has inspired recent research on bias detection, including “weasel words” and “hedges”. Yet to date, little work has been done on identifying “puffery,” phrases that are overly positive without a verifiable source. We demonstrate that collecting training data for this task requires some care, and construct a dataset by combining Wikipedia editorial annotations and information retrieval techniques. We compare several approaches to predicting puffery, and achieve 0.963 f1 score by incorporating citation features into a RoBERTa model. Finally, we demonstrate how to integrate our model with Wikipedia’s public infrastructure to give back to the Wikipedia editor community.