no code implementations • Findings (ACL) 2021 • Elsbeth Turcan, Shuai Wang, Rishita Anubhai, Kasturi Bhattacharjee, Yaser Al-Onaizan, Smaranda Muresan
Detecting what emotions are expressed in text is a well-studied problem in natural language processing.
1 code implementation • NAACL 2021 • Elsbeth Turcan, Smaranda Muresan, Kathleen McKeown
The problem of detecting psychological stress in online posts, and more broadly, of detecting people in distress or in need of help, is a sensitive application for which the ability to interpret models is vital.
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 • WS 2019 • Elsbeth Turcan, Kathleen McKeown
Stress is a nigh-universal human experience, particularly in the online world.