no code implementations • EMNLP (Louhi) 2020 • Zhengping Jiang, Sarah Ita Levitan, Jonathan Zomick, Julia Hirschberg
We address the problem of automatic detection of psychiatric disorders from the linguistic content of social media posts.
no code implementations • NAACL (CLPsych) 2021 • Zhengping Jiang, Jonathan Zomick, Sarah Ita Levitan, Mark Serper, Julia Hirschberg
We address the problem of predicting psychiatric hospitalizations using linguistic features drawn from social media posts.
no code implementations • EACL 2021 • David Wan, Chris Kedzie, Faisal Ladhak, Elsbeth Turcan, Petra Galuščáková, Elena Zotkina, Zhengping Jiang, Peter Bell, Kathleen McKeown
Typical ASR systems segment the input audio into utterances using purely acoustic information, which may not resemble the sentence-like units that are expected by conventional machine translation (MT) systems for Spoken Language Translation.
no code implementations • 19 Oct 2020 • David Wan, Zhengping Jiang, Chris Kedzie, Elsbeth Turcan, Peter Bell, Kathleen McKeown
In this work, we focus on improving ASR output segmentation in the context of low-resource language speech-to-text translation.
no code implementations • LREC 2020 • David Wan, Zhengping Jiang, Chris Kedzie, Elsbeth Turcan, Peter Bell, Kathy Mckeown
In this work, we focus on improving ASR output segmentation in the context of low-resource language speech-to-text translation.
no code implementations • ACL 2020 • Tongfei Chen, Zhengping Jiang, Adam Poliak, Keisuke Sakaguchi, Benjamin Van Durme
We introduce Uncertain Natural Language Inference (UNLI), a refinement of Natural Language Inference (NLI) that shifts away from categorical labels, targeting instead the direct prediction of subjective probability assessments.
no code implementations • SEMEVAL 2018 • Zhengping Jiang, Qi Sun
In this document we present an end-to-end machine reading comprehension system that solves multiple choice questions with a textual entailment perspective.