Search Results for author: Shabnam Tafreshi

Found 11 papers, 0 papers with code

WASSA 2022 Shared Task: Predicting Empathy, Emotion and Personality in Reaction to News Stories

no code implementations WASSA (ACL) 2022 Valentin Barriere, Shabnam Tafreshi, João Sedoc, Sawsan Alqahtani

This paper presents the results that were obtained from WASSA 2022 shared task on predicting empathy, emotion, and personality in reaction to news stories.

You’ve translated it, now what?

no code implementations AMTA 2022 Michael Maxwell, Shabnam Tafreshi, Aquia Richburg, Balaji Kodali, Kymani Brown

In theory, this also provides style information such as bolding and italicization, but in practice, this capability is limited.

Machine Translation Optical Character Recognition (OCR)

Emotion Classification in Low and Moderate Resource Languages

no code implementations28 Feb 2024 Shabnam Tafreshi, Shubham Vatsal, Mona Diab

There are 7100+ active languages spoken around the world and building emotion classification for each language is labor intensive.

Classification Cross-Lingual Transfer +2

Can GPT Improve the State of Prior Authorization via Guideline Based Automated Question Answering?

no code implementations28 Feb 2024 Shubham Vatsal, Ayush Singh, Shabnam Tafreshi

Health insurance companies have a defined process called prior authorization (PA) which is a health plan cost-control process that requires doctors and other healthcare professionals to get clearance in advance from a health plan before performing a particular procedure on a patient in order to be eligible for payment coverage.

Question Answering Text Generation

Empathic Conversations: A Multi-level Dataset of Contextualized Conversations

no code implementations25 May 2022 Damilola Omitaomu, Shabnam Tafreshi, Tingting Liu, Sven Buechel, Chris Callison-Burch, Johannes Eichstaedt, Lyle Ungar, João Sedoc

Hence, we collected detailed characterization of the participants' traits, their self-reported empathetic response to news articles, their conversational partner other-report, and turn-by-turn third-party assessments of the level of self-disclosure, emotion, and empathy expressed.

The ARIEL-CMU Systems for LoReHLT18

no code implementations24 Feb 2019 Aditi Chaudhary, Siddharth Dalmia, Junjie Hu, Xinjian Li, Austin Matthews, Aldrian Obaja Muis, Naoki Otani, Shruti Rijhwani, Zaid Sheikh, Nidhi Vyas, Xinyi Wang, Jiateng Xie, Ruochen Xu, Chunting Zhou, Peter J. Jansen, Yiming Yang, Lori Levin, Florian Metze, Teruko Mitamura, David R. Mortensen, Graham Neubig, Eduard Hovy, Alan W. black, Jaime Carbonell, Graham V. Horwood, Shabnam Tafreshi, Mona Diab, Efsun S. Kayi, Noura Farra, Kathleen McKeown

This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).

Machine Translation Translation

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