no code implementations • 13 Nov 2020 • Amith Ananthram, Kailash Karthik Saravanakumar, Jessica Huynh, Homayoon Beigi
To address these two challenges, we present a multi-modal approach that first transfers learning from related tasks in speech and text to produce robust neural embeddings and then uses these embeddings to train a pLDA classifier that is able to adapt to previously unseen emotions and domains.
no code implementations • COLING 2020 • Amith Ananthram, Emily Allaway, Kathleen McKeown
General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks.
no code implementations • 4 Dec 2020 • Amith Ananthram, Emily Allaway, Kathleen McKeown
General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks.
no code implementations • 23 May 2022 • Anish Saha, Amith Ananthram, Emily Allaway, Heng Ji, Kathleen McKeown
Practitioners from many disciplines (e. g., political science) use expert-crafted taxonomies to make sense of large, unlabeled corpora.
1 code implementation • 29 May 2023 • Gengyu Wang, Kate Harwood, Lawrence Chillrud, Amith Ananthram, Melanie Subbiah, Kathleen McKeown
We present a new fact-checking benchmark, Check-COVID, that requires systems to verify claims about COVID-19 from news using evidence from scientific articles.
no code implementations • 26 Feb 2024 • Todd Morrill, Zhaoyuan Deng, Yanda Chen, Amith Ananthram, Colin Wayne Leach, Kathleen McKeown
Based on these results showing the utility of social orientation tags for dialogue outcome prediction tasks, we release our data sets, code, and models that are fine-tuned to predict social orientation tags on dialogue utterances.