Revanth Rameshkumar


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Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset
Revanth Rameshkumar | Peter Bailey
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper describes the Critical Role Dungeons and Dragons Dataset (CRD3) and related analyses. Critical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game. The dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding abstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player collaboration and spoken interaction. For each dialogue, there are a large number of turns, multiple abstractive summaries with varying levels of detail, and semantic ties to the previous dialogues. In addition, we provide a data augmentation method that produces 34,243 summary-dialogue chunk pairs to support current neural ML approaches, and we provide an abstractive summarization benchmark and evaluation.


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Assigning people to tasks identified in email: The EPA dataset for addressee tagging for detected task intent
Revanth Rameshkumar | Peter Bailey | Abhishek Jha | Chris Quirk
Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text

We describe the Enron People Assignment (EPA) dataset, in which tasks that are described in emails are associated with the person(s) responsible for carrying out these tasks. We identify tasks and the responsible people in the Enron email dataset. We define evaluation methods for this challenge and report scores for our model and naïve baselines. The resulting model enables a user experience operating within a commercial email service: given a person and a task, it determines if the person should be notified of the task.