1 code implementation • WASSA (ACL) 2022 • Allison Lahnala, Charles Welch, Lucie Flek
We build a system that leverages adapters, a light weight and efficient method for leveraging large language models to perform the task Em- pathy and Distress prediction tasks for WASSA 2022.
no code implementations • 28 Aug 2023 • Fabian Lechner, Allison Lahnala, Charles Welch, Lucie Flek
The potential to provide patients with faster information access while allowing medical specialists to concentrate on critical tasks makes medical domain dialog agents appealing.
no code implementations • 29 Oct 2022 • Allison Lahnala, Charles Welch, David Jurgens, Lucie Flek
We review the state of research on empathy in natural language processing and identify the following issues: (1) empathy definitions are absent or abstract, which (2) leads to low construct validity and reproducibility.
no code implementations • NAACL 2022 • Allison Lahnala, Charles Welch, Béla Neuendorf, Lucie Flek
Large pre-trained neural language models have supported the effectiveness of many NLP tasks, yet are still prone to generating toxic language hindering the safety of their use.
1 code implementation • LREC 2022 • Flora Sakketou, Allison Lahnala, Liane Vogel, Lucie Flek
The dataset includes a sufficient amount of stance polarity and intensity labels per user over time and within entire conversational threads, thus making subtle opinion fluctuations detectable both in long term and in short term.
no code implementations • 15 Oct 2021 • Kim Breitwieser, Allison Lahnala, Charles Welch, Lucie Flek, Martin Potthast
We introduce the problem of proficiency modeling: Given a user's posts on a social media platform, the task is to identify the subset of posts or topics for which the user has some level of proficiency.
1 code implementation • Findings (ACL) 2021 • Allison Lahnala, Yuntian Zhao, Charles Welch, Jonathan K. Kummerfeld, Lawrence An, Kenneth Resnicow, Rada Mihalcea, Verónica Pérez-Rosas
A growing number of people engage in online health forums, making it important to understand the quality of the advice they receive.
no code implementations • 4 Feb 2021 • Allison Lahnala, Gauri Kambhatla, Jiajun Peng, Matthew Whitehead, Gillian Minnehan, Eric Guldan, Jonathan K. Kummerfeld, Anıl Çamcı, Rada Mihalcea
In the first case study, we demonstrate that using chord embeddings in a next chord prediction task yields predictions that more closely match those by experienced musicians.
no code implementations • EMNLP (NLP-COVID19) 2020 • Charles Welch, Allison Lahnala, Verónica Pérez-Rosas, Siqi Shen, Sarah Seraj, Larry An, Kenneth Resnicow, James Pennebaker, Rada Mihalcea
The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability.