no code implementations • WS 2018 • Jackson Luken, Nanjiang Jiang, Marie-Catherine de Marneffe
This paper describes our system submission to the 2018 Fact Extraction and VERification (FEVER) shared task.
no code implementations • ACL 2019 • Nanjiang Jiang, Marie-Catherine de Marneffe
Here, we explore the hypothesis that linguistic deficits drive the error patterns of existing speaker commitment models by analyzing the linguistic correlates of model error on a challenging naturalistic dataset.
no code implementations • WS 2019 • Byung-Doh Oh, Pranav Maneriker, Nanjiang Jiang
This paper describes the OSU submission to the SIGMORPHON 2019 shared task, Crosslinguality and Context in Morphology.
no code implementations • IJCNLP 2019 • Nanjiang Jiang, Marie-Catherine de Marneffe
Natural language inference (NLI) datasets (e. g., MultiNLI) were collected by soliciting hypotheses for a given premise from annotators.
1 code implementation • 2 Jul 2021 • Nanjiang Jiang, Marie-Catherine de Marneffe
We investigate how well BERT performs on predicting factuality in several existing English datasets, encompassing various linguistic constructions.
no code implementations • Findings (EMNLP) 2021 • Jeremy R. Cole, Nanjiang Jiang, Panupong Pasupat, Luheng He, Peter Shaw
The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders.
no code implementations • DeepLo 2022 • Melanie Rubino, Nicolas Guenon des Mesnards, Uday Shah, Nanjiang Jiang, Weiqi Sun, Konstantine Arkoudas
However, a single model is still typically trained and deployed for each task separately, requiring labeled training data for each, which makes it challenging to support new tasks, even within a single business vertical (e. g., food-ordering or travel booking).