title = “718” date = 2022-05-10T21:19:49-05:00 draft = false

Authors. Comma separated list, e.g. ["Bob Smith", "David Jones"].

authors = [“Puneet Mathur”, “Vlad I Morariu”, “Verena Kaynig-Fittkau”, “Jiuxiang Gu”, “Franck Dernoncourt”, “Quan Hung Tran”, “Ani Nenkova”, “Dinesh Manocha”, “Rajiv Jain”]

Publication type.

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1 = Conference paper

2 = Journal article

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6 = Book section

publication_types = [“1”]

Publication name and optional abbreviated version.

publication = “DocTime: A Document-level Temporal Dependency Graph Parser”

Abstract and optional shortened version.

abstract = “We introduce DocTime - a novel temporal dependency graph (TDG) parser that takes as input a text document and produces a temporal dependency graph. It outperforms previous BERT-based solutions by a relative 4-8% on three datasets from modeling the problem as a graph network with path-prediction loss to incorporate longer range dependencies. This work also demonstrates how the TDG graph can be used to improve the downstream tasks of temporal questions answering and NLI by a relative 4-10% with a new framework that incorporates the temporal dependency graph into the self-attention layer of Transformer models (Time-transformer). Finally, we develop and evaluate on a new temporal dependency graph dataset for the domain of contractual documents, which has not been previously explored in this setting. “

Proceedings

e.g. “IEEE International Conference on Robotics and Automation (ICRA)”

proceedings = “2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics”

The short name with the year. e.g. ICASSP, 2021

proceedings_short = “NAACL, 2022”

Volume

Number

number = “”

Start Page

start_page = “”

End Page

end_page = “”

Publisher

publisher = “”

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selected = false

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projects = []

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tags = []

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math = false