no code implementations • WS 2019 • Sheshera Mysore, Zach Jensen, Edward Kim, Kevin Huang, Haw-Shiuan Chang, Emma Strubell, Jeffrey Flanigan, Andrew McCallum, Elsa Olivetti
Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text.
no code implementations • 21 Nov 2020 • Vrindavan Harrison, Juraj Juraska, Wen Cui, Lena Reed, Kevin K. Bowden, Jiaqi Wu, Brian Schwarzmann, Abteen Ebrahimi, Rishi Rajasekaran, Nikhil Varghese, Max Wechsler-Azen, Steve Whittaker, Jeffrey Flanigan, Marilyn Walker
This report describes Athena, a dialogue system for spoken conversation on popular topics and current events.
no code implementations • 20 May 2021 • Geetanjali Rakshit, Jeffrey Flanigan
We introduce ASQ, a tool to automatically mine questions and answers from a sentence using the Abstract Meaning Representation (AMR).
no code implementations • ACL 2021 • Wenchao Du, Jeffrey Flanigan
We propose methods for excluding parts of Gigaword to remove this overlap, and show that our approach leads to a more realistic evaluation of the task of AMR-to-text generation.
no code implementations • 14 Jul 2022 • Nilay Patel, Jeffrey Flanigan
Human language is known to exhibit a nested, hierarchical structure, allowing us to form complex sentences out of smaller pieces.
no code implementations • 30 Nov 2022 • Ritu Belani, Jeffrey Flanigan
We build the first system (to our knowledge) to automatically identify a wide range of motivations that speakers code-switch in everyday speech, achieving an accuracy of 75% across all motivations.
no code implementations • 25 Feb 2023 • Jon Z. Cai, Brendan King, Margaret Perkoff, Shiran Dudy, Jie Cao, Marie Grace, Natalia Wojarnik, Ananya Ganesh, James H. Martin, Martha Palmer, Marilyn Walker, Jeffrey Flanigan
DDA combines and adapts features from existing dialogue annotation frameworks, and emphasizes the multi-relational response structure of dialogues in addition to the dialogue acts and rhetorical relations.
no code implementations • 24 Sep 2023 • Geetanjali Rakshit, Jeffrey Flanigan
Yes/no questions, in particular, are a useful probe of figurative language understanding capabilities of large language models.
no code implementations • 6 Dec 2023 • Chris Yuhao Liu, Jeffrey Flanigan
Our results suggest the following implication: Double descent is unlikely to be a problem for real-world machine learning setups.
no code implementations • 26 Dec 2023 • Changmao Li, Jeffrey Flanigan
Large language models (LLMs) offer impressive performance in various zero-shot and few-shot tasks.
1 code implementation • NAACL (DLG4NLP) 2022 • Changmao Li, Jeffrey Flanigan
Previous studies have shown that the Abstract Meaning Representation (AMR) can improve Neural Machine Translation (NMT).
1 code implementation • 16 Apr 2024 • Changmao Li, Jeffrey Flanigan
While there are many automated systems for predicting future numerical data, such as weather, stock prices, and demand for products, there is relatively little work in automatically predicting textual data.
1 code implementation • 23 Apr 2024 • Brendan King, Jeffrey Flanigan
Training task-oriented dialogue systems typically requires turn-level annotations for interacting with their APIs: e. g. a dialogue state and the system actions taken at each step.
1 code implementation • 4 Jul 2023 • Brendan King, Jeffrey Flanigan
There has been significant interest in zero and few-shot learning for dialogue state tracking (DST) due to the high cost of collecting and annotating task-oriented dialogues.
1 code implementation • 12 Oct 2023 • Nilay Patel, Rahul Saha, Jeffrey Flanigan
This is a challenging task, and especially for higher-level mathematics found in research papers.
1 code implementation • NAACL 2022 • Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O'Gorman, Young-suk Lee, Jeffrey Flanigan, Ramón Fernandez Astudillo, Radu Florian, Salim Roukos, Nathan Schneider
Despite extensive research on parsing of English sentences into Abstraction Meaning Representation (AMR) graphs, which are compared to gold graphs via the Smatch metric, full-document parsing into a unified graph representation lacks well-defined representation and evaluation.
1 code implementation • HLT 2015 • Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman Sadeh, Noah A. Smith
We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR).