Papers by Shantanu Agarwal

2 papers
End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs (2021.emnlp-main)

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Challenge: Existing systems that use human-to-human dialogs to help users with specific tasks are still unexplored.
Approach: They propose a problem in which a dialog system mimics a troubleshooting agent . they use a dataset grounded on 12 different troubleshooking flowcharts to train the agent a neural model .
Outcome: The proposed model can do zero-shot transfer to unseen flowcharts and sets a strong baseline for future research.
Massively Multi-Lingual Event Understanding: Extraction, Visualization, and Search (2023.acl-demo)

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Challenge: Using only English training data, ISI-Clear makes global events available on-demand in 100 languages . Using a fixed task, events may still shift from day to day .
Approach: They propose a cross-lingual zero-shot event extraction system that makes global events available on-demand in 100 languages.
Outcome: The proposed system can extract events from non-English documents in 100 languages.

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