2 code implementations • 28 Mar 2024 • Manuel Tonneau, Pedro Vitor Quinta de Castro, Karim Lasri, Ibrahim Farouq, Lakshminarayanan Subramanian, Victor Orozco-Olvera, Samuel P. Fraiberger
Finally, owing to the modest performance of HSD systems in real-world conditions, we find that content moderators would need to review about ten thousand Nigerian tweets flagged as hateful daily to moderate 60% of all hateful content, highlighting the challenges of moderating hate speech at scale as social media usage continues to grow globally.
no code implementations • 6 Mar 2024 • Rishabh Adiga, Lakshminarayanan Subramanian, Varun Chandrasekaran
This approach avoids the training of a dedicated model for selection of examples, and instead uses certain metrics to align the syntactico-semantic complexity of test sentences and examples.
no code implementations • 29 Nov 2023 • Sonish Sivarajkumar, Pratyush Tandale, Ankit Bhardwaj, Kipp W. Johnson, Anoop Titus, Benjamin S. Glicksberg, Shameer Khader, Kamlesh K. Yadav, Lakshminarayanan Subramanian
We constructed a knowledge graph of 81, 488 unique TF cascades, with the longest cascade consisting of 62 TFs.
no code implementations • 17 Nov 2021 • Ananth Balashankar, Lakshminarayanan Subramanian, Samuel P. Fraiberger
Anticipating the outbreak of a food crisis is crucial to efficiently allocate emergency relief and reduce human suffering.
no code implementations • ACL 2021 • Ananth Balashankar, Lakshminarayanan Subramanian
By incorporating these faithfulness properties, we learn text embeddings that are 31. 3{\%} more faithful to human validated causal graphs with about 800K and 200K causal links and achieve 21. 1{\%} better Precision-Recall AUC in a link prediction fine-tuning task.
no code implementations • 7 Jan 2021 • Yufang Huang, Kelly M. Axsom, John Lee, Lakshminarayanan Subramanian, Yiye Zhang
Following the representation learning and clustering steps, we embed the objective function in DICE with a constraint which requires a statistically significant association between the outcome and cluster membership of learned representations.
no code implementations • 1 Oct 2020 • Yan Shvartzshnaider, Ananth Balashankar, Vikas Patidar, Thomas Wies, Lakshminarayanan Subramanian
This paper formulates a new task of extracting privacy parameters from a privacy policy, through the lens of Contextual Integrity, an established social theory framework for reasoning about privacy norms.
no code implementations • IJCNLP 2019 • Ananth Balashankar, Sun Chakraborty, an, Samuel Fraiberger, Lakshminarayanan Subramanian
We propose a new framework to uncover the relationship between news events and real world phenomena.
no code implementations • 30 Oct 2019 • Ananth Balashankar, Alyssa Lees, Chris Welty, Lakshminarayanan Subramanian
The potential for learned models to amplify existing societal biases has been broadly recognized.
no code implementations • WS 2018 • Ananth Balashankar, Sun Chakraborty, an, Lakshminarayanan Subramanian
We present Word Influencer Networks (WIN), a graph framework to extract longitudinal temporal relationships between any pair of informative words from news streams.
Relationship Extraction (Distant Supervised) Stock Price Prediction
no code implementations • WS 2018 • Yan Shvartzshanider, Ananth Balashankar, Thomas Wies, Lakshminarayanan Subramanian
We describe our experiences in using an open domain question answering model (Chen et al., 2017) to evaluate an out-of-domain QA task of assisting in analyzing privacy policies of companies.
no code implementations • 25 Jan 2017 • Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman
We consider the problem of segmenting a large population of customers into non-overlapping groups with similar preferences, using diverse preference observations such as purchases, ratings, clicks, etc.
no code implementations • NeurIPS 2014 • Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman
In this paper, we study the problem of aggregating noisy labels from crowd workers to infer the underlying true labels of binary tasks.