1 code implementation • 31 Oct 2022 • Georgios Chochlakis, Gireesh Mahajan, Sabyasachee Baruah, Keith Burghardt, Kristina Lerman, Shrikanth Narayanan
In this work, we study how we can build a single model that can transition between these different configurations by leveraging multilingual models and Demux, a transformer-based model whose input includes the emotions of interest, enabling us to dynamically change the emotions predicted by the model.
1 code implementation • 28 Oct 2022 • Georgios Chochlakis, Gireesh Mahajan, Sabyasachee Baruah, Keith Burghardt, Kristina Lerman, Shrikanth Narayanan
First, we develop two modeling approaches to the problem in order to capture word associations of the emotion words themselves, by either including the emotions in the input, or by leveraging Masked Language Modeling (MLM).
no code implementations • 11 Oct 2021 • Justin Olah, Sabyasachee Baruah, Digbalay Bose, Shrikanth Narayanan
Emotion recognition from text is a challenging task due to diverse emotion taxonomies, lack of reliable labeled data in different domains, and highly subjective annotation standards.
1 code implementation • 8 Oct 2021 • Sabyasachee Baruah, Krishna Somandepalli, Shrikanth Narayanan
We analyze the frequency and sentiment trends of different occupations, study the effect of media attributes like genre, country of production, and title type on these trends, and investigate if the incidence of professions in media subtitles correlate with their real-world employment statistics.
no code implementations • EACL 2021 • Jasabanta Patro, Sabyasachee Baruah
There is a huge difference between a scientific journal reporting {`}wine consumption might be correlated to cancer{'}, and a media outlet publishing {`}wine causes cancer{'} citing the journal{'}s results.