no code implementations • ACL 2022 • Ramit Sawhney, Atula Neerkaje, Manas Gaur
Recent studies have shown that social media has increasingly become a platform for users to express suicidal thoughts outside traditional clinical settings.
1 code implementation • ACL 2022 • Shivam Agarwal, Ramit Sawhney, Sanchit Ahuja, Ritesh Soun, Sudheer Chava
Analyzing the temporal sequence of texts from sources such as social media, news, and parliamentary debates is a challenging problem as it exhibits time-varying scale-free properties and fine-grained timing irregularities.
1 code implementation • ACL 2022 • Ramit Sawhney, Megh Thakkar, Shrey Pandit, Ritesh Soun, Di Jin, Diyi Yang, Lucie Flek
Interpolation-based regularisation methods such as Mixup, which generate virtual training samples, have proven to be effective for various tasks and modalities. We extend Mixup and propose DMix, an adaptive distance-aware interpolative Mixup that selects samples based on their diversity in the embedding space.
1 code implementation • NAACL 2022 • Ramit Sawhney, Ritesh Soun, Shrey Pandit, Megh Thakkar, Sarvagya Malaviya, Yuval Pinter
CIAug achieves state-of-the-art results over existing interpolative augmentation methods on 10 benchmark datasets across 4 languages in text classification and named-entity recognition tasks.
no code implementations • EMNLP 2020 • Ramit Sawhney, Harshit Joshi, Saumya Gandhi, Rajiv Ratn Shah
Understanding the build-up of such ideation is critical for the identification of at-risk users and suicide prevention.
1 code implementation • EMNLP 2020 • Ramit Sawhney, Piyush Khanna, Arshiya Aggarwal, Taru Jain, Puneet Mathur, Rajiv Ratn Shah
Natural language processing has recently made stock movement forecasting and volatility forecasting advances, leading to improved financial forecasting.
1 code implementation • EMNLP 2020 • Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, Rajiv Ratn Shah
In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task.
Ranked #1 on Stock Market Prediction on stocknet (using extra training data)
1 code implementation • EMNLP 2021 • Ramit Sawhney, Megh Thakkar, Shivam Agarwal, Di Jin, Diyi Yang, Lucie Flek
Interpolation-based regularisation methods for data augmentation have proven to be effective for various tasks and modalities.
no code implementations • EMNLP (MRL) 2021 • Ramit Sawhney, Megh Thakkar, Shrey Pandit, Debdoot Mukherjee, Lucie Flek
Interpolation-based regularisation methods have proven to be effective for various tasks and modalities.
no code implementations • EMNLP (MRL) 2021 • Megh Thakkar, Vishwa Shah, Ramit Sawhney, Debdoot Mukherjee
There have been efforts in cross-lingual transfer learning for various tasks.
no code implementations • 21 Oct 2024 • Manan Suri, Puneet Mathur, Franck Dernoncourt, Rajiv Jain, Vlad I Morariu, Ramit Sawhney, Preslav Nakov, Dinesh Manocha
Document structure editing involves manipulating localized textual, visual, and layout components in document images based on the user's requests.
no code implementations • 5 Sep 2022 • Ramit Sawhney, Atula Tejaswi Neerkaje, Ivan Habernal, Lucie Flek
Clinical NLP tasks such as mental health assessment from text, must take social constraints into account - the performance maximization must be constrained by the utmost importance of guaranteeing privacy of user data.
1 code implementation • NAACL 2022 • Ramit Sawhney, Shivam Agarwal, Vivek Mittal, Paolo Rosso, Vikram Nanda, Sudheer Chava
Further, we develop a set of sequence-to-sequence hyperbolic models suited to this multi-span identification task based on the power-law dynamics of cryptocurrencies and user behavior on social media.
1 code implementation • NAACL (PrivateNLP) 2022 • Victor Petrén Bach Hansen, Atula Tejaswi Neerkaje, Ramit Sawhney, Lucie Flek, Anders Søgaard
The performance cost of differential privacy has, for some applications, been shown to be higher for minority groups; fairness, conversely, has been shown to disproportionally compromise the privacy of members of such groups.
1 code implementation • 16 Feb 2022 • Vikram Gupta, Rini Sharon, Ramit Sawhney, Debdoot Mukherjee
Abusive content detection in spoken text can be addressed by performing Automatic Speech Recognition (ASR) and leveraging advancements in natural language processing.
no code implementations • ACL 2021 • Ramit Sawhney, Mihir Goyal, Prakhar Goel, Puneet Mathur, Rajiv Ratn Shah
We introduce M3ANet, a baseline architecture that takes advantage of the multimodal multi-speaker input to forecast the financial risk associated with the M{\&}A calls.
1 code implementation • NAACL 2021 • Ramit Sawhney, Puneet Mathur, Taru Jain, Akash Kumar Gautam, Rajiv Ratn Shah
We show how for more domain-specific tasks related to sexual abuse disclosures such as sarcasm identification and dialogue act (refutation, justification, allegation) classification, homogeneous multitask learning is helpful, whereas for more general tasks such as stance and hate speech detection, heterogeneous multitask learning with emotion classification works better.
no code implementations • NAACL 2021 • Ramit Sawhney, Harshit Joshi, Rajiv Ratn Shah, Lucie Flek
Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners.
1 code implementation • NAACL 2021 • Ramit Sawhney, Arshiya Aggarwal, Rajiv Ratn Shah
In this work, we present the first study to discover the gender bias in multimodal volatility prediction due to gender-sensitive audio features and fewer female executives in earnings calls of one of the world{'}s biggest stock indexes, the S{\&}P 500 index.
no code implementations • NAACL 2021 • Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah
It is challenging to design profitable and practical trading strategies, as stock price movements are highly stochastic, and the market is heavily influenced by chaotic data across sources like news and social media.
no code implementations • EACL 2021 • Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah
Designing profitable trading strategies is complex as stock movements are highly stochastic; the market is influenced by large volumes of noisy data across diverse information sources like news and social media.
1 code implementation • EACL 2021 • Ramit Sawhney, Harshit Joshi, Lucie Flek, Rajiv Ratn Shah
Building on clinical studies, PHASE learns phase-like progressions in users{'} historical Plutchik-wheel-based emotions to contextualize suicidal intent.
no code implementations • COLING 2020 • Amit Jindal, Arijit Ghosh Chowdhury, Aniket Didolkar, Di Jin, Ramit Sawhney, Rajiv Ratn Shah
Models with a large number of parameters are prone to over-fitting and often fail to capture the underlying input distribution.
no code implementations • COLING 2020 • Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah
Parliamentary debates present a valuable language resource for analyzing comprehensive options in electing representatives under a functional, free society.
no code implementations • 24 Jan 2020 • Gyanesh Anand, Akash Gautam, Puneet Mathur, Debanjan Mahata, Rajiv Ratn Shah, Ramit Sawhney
Twitter is a social media platform where users express opinions over a variety of issues.
no code implementations • 14 Dec 2019 • Akash Gautam, Puneet Mathur, Rakesh Gosangi, Debanjan Mahata, Ramit Sawhney, Rajiv Ratn Shah
In this paper, we present a dataset containing 9, 973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts.
no code implementations • ACL 2019 • Anjali Bhavan, Rohan Mishra, Pradyumna Prakhar Sinha, Ramit Sawhney, Rajiv Ratn Shah
Analyzing polarities and sentiments inherent in political speeches and debates poses an important problem today.
1 code implementation • ACL 2019 • Arijit Ghosh Chowdhury, Ramit Sawhney, Rajiv Ratn Shah, Debanjan Mahata
The availability of large-scale online social data, coupled with computational methods can help us answer fundamental questions relat- ing to our social lives, particularly our health and well-being.
no code implementations • ACL 2019 • Arijit Ghosh Chowdhury, Aniket Didolkar, Ramit Sawhney, Rajiv Ratn Shah
The rapid widespread of social media has lead to some undesirable consequences like the rapid increase of hateful content and offensive language.
no code implementations • NAACL 2019 • Rohan Mishra, Pradyumn Prakhar Sinha, Ramit Sawhney, Debanjan Mahata, Puneet Mathur, Rajiv Ratn Shah
Suicide is a leading cause of death among youth and the use of social media to detect suicidal ideation is an active line of research.
no code implementations • NAACL 2019 • Arijit Ghosh Chowdhury, Ramit Sawhney, Puneet Mathur, Debanjan Mahata, Rajiv Ratn Shah
The {\#}MeToo movement is an ongoing prevalent phenomenon on social media aiming to demonstrate the frequency and widespread of sexual harassment by providing a platform to speak narrate personal experiences of such harassment.
1 code implementation • WS 2018 • Puneet Mathur, Ramit Sawhney, Meghna Ayyar, Rajiv Shah
The use of code-switched languages (\textit{e. g.}, Hinglish, which is derived by the blending of Hindi with the English language) is getting much popular on Twitter due to their ease of communication in native languages.
no code implementations • WS 2018 • Ramit Sawhney, Manch, Prachi a, Puneet Mathur, Rajiv Shah, Raj Singh
The increasing suicide rates amongst youth and its high correlation with suicidal ideation expression on social media warrants a deeper investigation into models for the detection of suicidal intent in text such as tweets to enable prevention.
no code implementations • WS 2018 • Puneet Mathur, Rajiv Shah, Ramit Sawhney, Debanjan Mahata
The paper focuses on the classification of offensive tweets written in Hinglish language, which is a portmanteau of the Indic language Hindi with the Roman script.
no code implementations • ACL 2018 • Ramit Sawhney, Manch, Prachi a, Raj Singh, Swati Aggarwal
Technological advancements in the World Wide Web and social networks in particular coupled with an increase in social media usage has led to a positive correlation between the exhibition of Suicidal ideation on websites such as Twitter and cases of suicide.