Ritvik Shrivastava


“Laughing at you or with you”: The Role of Sarcasm in Shaping the Disagreement Space
Debanjan Ghosh | Ritvik Shrivastava | Smaranda Muresan
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved. Users often use figurative language, such as sarcasm, either as persuasive devices or to attack the opponent by an ad hominem argument. To further our understanding of the role of sarcasm in shaping the disagreement space, we present a thorough experimental setup using a corpus annotated with both argumentative moves (agree/disagree) and sarcasm. 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). We demonstrate that modeling sarcasm improves the argumentative relation classification task (agree/disagree/none) in all setups.


SemTagger: A Novel Approach for Semantic Similarity Based Hashtag Recommendation on Twitter
Kuntal Dey | Ritvik Shrivastava | Saroj Kaushik | L Venkata Subramaniam
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)


A Paraphrase and Semantic Similarity Detection System for User Generated Short-Text Content on Microblogs
Kuntal Dey | Ritvik Shrivastava | Saroj Kaushik
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Existing systems deliver high accuracy and F1-scores for detecting paraphrase and semantic similarity on traditional clean-text corpus. For instance, on the clean-text Microsoft Paraphrase benchmark database, the existing systems attain an accuracy as high as 0:8596. However, existing systems for detecting paraphrases and semantic similarity on user-generated short-text content on microblogs such as Twitter, comprising of noisy and ad hoc short-text, needs significant research attention. In this paper, we propose a machine learning based approach towards this. 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. We apply support vector machine (SVM) based learning. We use the benchmark Twitter paraphrase data, released as a part of SemEval 2015, for experiments. Our system delivers a paraphrase detection F1-score of 0.717 and semantic similarity detection F1-score of 0.741, thereby significantly outperforming the existing systems, that deliver F1-scores of 0.696 and 0.724 for the two problems respectively. Our features also allow us to obtain a rank among the top-10, when trained on the Microsoft Paraphrase corpus and tested on the corresponding test data, thereby empirically establishing our approach as ubiquitous across the different paraphrase detection databases.