no code implementations • NAACL (SocialNLP) 2021 • Shamik Roy, Dan Goldwasser
Then, qualitative and quantitative evaluations using the corpus show that there is a strong correlation between the moral foundation usage and the politicians’ nuanced stance on a particular topic.
no code implementations • 9 Mar 2024 • Shamik Roy, Sailik Sengupta, Daniele Bonadiman, Saab Mansour, Arshit Gupta
To study this, we propose the problem of faithful planning in TODs that needs to resolve user intents by following predefined flows and preserving API dependencies.
no code implementations • 11 Oct 2023 • Shamik Roy, Dan Goldwasser
We convert the text to a graph by breaking it into structured elements and connect it with the social network of authors, then structured prediction is done over the elements for identifying perspectives.
no code implementations • 16 Feb 2023 • Shamik Roy, Raphael Shu, Nikolaos Pappas, Elman Mansimov, Yi Zhang, Saab Mansour, Dan Roth
Conventional text style transfer approaches focus on sentence-level style transfer without considering contextual information, and the style is described with attributes (e. g., formality).
no code implementations • 3 Feb 2023 • Shamik Roy, Nishanth Sridhar Nakshatri, Dan Goldwasser
Data scarcity is a common problem in NLP, especially when the annotation pertains to nuanced socio-linguistic concepts that require specialized knowledge.
1 code implementation • 19 Oct 2022 • Tunazzina Islam, Shamik Roy, Dan Goldwasser
Social media platforms are currently the main channel for political messaging, allowing politicians to target specific demographics and adapt based on their reactions.
1 code implementation • EMNLP 2021 • Shamik Roy, Maria Leonor Pacheco, Dan Goldwasser
Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions.
1 code implementation • EMNLP 2020 • Shamik Roy, Dan Goldwasser
In this paper we suggest a minimally-supervised approach for identifying nuanced frames in news article coverage of politically divisive topics.