1 code implementation • EACL 2021 • Debanjan Ghosh, Ritvik Shrivastava, Smaranda Muresan
We exploit joint modeling in terms of (a) applying discrete features that are useful in detecting sarcasm to the task of argumentative relation classification (agree/disagree/none), and (b) multitask learning for argumentative relation classification and sarcasm detection using deep learning architectures (e. g., dual Long Short-Term Memory (LSTM) with hierarchical attention and Transformer-based architectures).
1 code implementation • 26 Jan 2021 • Debanjan Ghosh, Ritvik Shrivastava, Smaranda Muresan
We exploit joint modeling in terms of (a) applying discrete features that are useful in detecting sarcasm to the task of argumentative relation classification (agree/disagree/none), and (b) multitask learning for argumentative relation classification and sarcasm detection using deep learning architectures (e. g., dual Long Short-Term Memory (LSTM) with hierarchical attention and Transformer-based architectures).
no code implementations • 9 Jan 2018 • Kuntal Dey, Ritvik Shrivastava, Saroj Kaushik
The topical stance detection problem addresses detecting the stance of the text content with respect to a given topic: whether the sentiment of the given text content is in FAVOR of (positive), is AGAINST (negative), or is NONE (neutral) towards the given topic.
1 code implementation • 5 Dec 2017 • Kuntal Dey, Ritvik Shrivastava, Saroj Kaushik, L. Venkata Subramaniam
The top-K hashtags that appear in this ranked list, are recommended for the given test post.
no code implementations • 25 Nov 2017 • Anand Gupta, Hardeo Thakur, Ritvik Shrivastava, Pulkit Kumar, Sreyashi Nag
In this paper, we propose a novel framework that combines the distributive computational abilities of Apache Spark and the advanced machine learning architecture of a deep multi-layer perceptron (MLP), using the popular concept of Cascade Learning.
no code implementations • COLING 2016 • Kuntal Dey, Ritvik Shrivastava, Saroj Kaushik
We propose a set of features that, although well-known in the NLP literature for solving other problems, have not been explored for detecting paraphrase or semantic similarity, on noisy user-generated short-text data such as Twitter.