MetRoBERTa: Leveraging Traditional Customer Relationship Management Data to Develop a Transit-Topic-Aware Language Model by Leong, Michael, Abdelhalim, Awad, Ha, Jude, Patterson, Diane, Pincus, Gabriel L, Harris, Anthony B, Eichler, Michael, Zhao, Jinhua

“…First, we utilize semi-supervised learning to engineer a training dataset of 11 broad transit topics detected in a corpus of 6 years of customer feedback provided to the Washington Metropolitan Area Transit Authority (WMATA). We then use this dataset to train and thoroughly evaluate a language model based on the RoBERTa architecture. …”
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