Search Results for author:
Found 6 papers, 3 papers with code
"We Demand Justice!": Towards Social Context Grounding of Political Texts
Social media discourse frequently consists of 'seemingly similar language used by opposing sides of the political spectrum', often translating to starkly contrasting perspectives.
Reinforcement Guided Multi-Task Learning Framework for Low-Resource Stereotype Detection
Further, we present a multi-task model that leverages the abundance of data-rich neighboring tasks such as hate speech detection, offensive language detection, misogyny detection, etc., to improve the empirical performance on "Stereotype Detection".
A Novel Two-stage Framework for Extracting Opinionated Sentences from News Articles
This paper presents a novel two-stage framework to extract opinionated sentences from a given news article.
Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy
In this paper, we propose a hybrid technique for semantic question matching.
Using Natural Language Relations between Answer Choices for Machine Comprehension
We use a stand-alone question answering (QA) system to perform QA task and a Natural Language Inference (NLI) system to identify the relations between the choice pairs.
Understanding Politics via Contextualized Discourse Processing
In this paper, we propose a Compositional Reader model consisting of encoder and composer modules, that attempts to capture and leverage such information to generate more effective representations for entities, issues, and events.
