Papers by Ramit Sawhney
An Empirical Investigation of Bias in the Multimodal Analysis of Financial Earnings Calls (2021.naacl-main)
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| Challenge: | Existing research focuses on textual elements of financial disclosures but ignores the rich acoustic features in the executives’ speech. |
| Approach: | They propose to use a multimodal approach that leverages the verbal and vocal cues of speakers in financial disclosures to predict volatility and risk. |
| Outcome: | The proposed models outperform existing models in the financial realm but still underrepresent the diverse communities spanning demographics, gender, and native speech. |
SNAP-BATNET: Cascading Author Profiling and Social Network Graphs for Suicide Ideation Detection on Social Media (N19-3)
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Rohan Mishra, Pradyumn Prakhar Sinha, Ramit Sawhney, Debanjan Mahata, Puneet Mathur, Rajiv Ratn Shah
| Challenge: | Suicide is a leading cause of death among youth worldwide and currently only uses text-based cues to detect suicidal ideation. |
| Approach: | They propose a deep learning based model to extract text-based features from tweets and a novel Feature Stacking approach to combine other community-based information. |
| Outcome: | The proposed model outperforms existing models on an annotated dataset of tweets using a three-phase strategy and proposes a novel Feature Stacking approach to combine other community-based information such as historical author profiling and graph embeddings. |
Investigating Political Herd Mentality: A Community Sentiment Based Approach (P19-2)
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| Challenge: | polarities inherent in political speeches and debates pose an important problem today. |
| Approach: | They propose to use community-based graphs to augment hand-crafted features based on topic modeling and emotion detection on debate transcripts. |
| Outcome: | The proposed approach surpasses the benchmark results on the same dataset. |
CIAug: Equipping Interpolative Augmentation with Curriculum Learning (2022.naacl-main)
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| Challenge: | Current methods for interpolative data augmentation select samples at random, which might make it difficult for the model to generalize better and converge faster. |
| Approach: | They propose a curriculum-based learning method that leverages the relative position of samples in hyperbolic embedding space as a complexity measure to gradually mix up increasingly difficult and diverse samples along training. |
| Outcome: | The proposed method achieves state-of-the-art results over existing methods on 10 benchmark datasets across 4 languages in text classification and named-entity recognition tasks. |
DocScript: Document-level Script Event Prediction (2024.lrec-main)
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Puneet Mathur, Vlad I. Morariu, Aparna Garimella, Franck Dernoncourt, Jiuxiang Gu, Ramit Sawhney, Preslav Nakov, Dinesh Manocha, Rajiv Jain
| Challenge: | Existing script event prediction frameworks such as ChatGPT and FlanT5 lack the ability to learn long-range dependencies between events. |
| Approach: | They propose a novel script event prediction task which aims to predict the next event from a candidate list of narrative events in long-form documents. |
| Outcome: | The proposed architecture can learn sequential ordering between events at the document scale. |
A Risk-Averse Mechanism for Suicidality Assessment on Social Media (2022.acl-short)
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| Challenge: | Social media has become a platform for users to express suicidal thoughts outside traditional clinical settings. |
| Approach: | They propose a risk-averse hierarchical attention classifier that refrains from making uncertain predictions on real-world Reddit data. |
| Outcome: | The proposed system can refrain from 83% of incorrect predictions on real-world Reddit data. |
The Impact of Differential Privacy on Group Disparity Mitigation (2024.findings-naacl)
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| Challenge: | a recent study evaluated the impact of differential privacy on fairness across four tasks. |
| Approach: | They evaluate the impact of differential privacy on fairness across four diverse tasks . they train (,)-differentially private models with empirical risk minimization . |
| Outcome: | The proposed model shows that differential privacy increases performance differences between groups . the model also reduces performance differences in the robust setting . |
Saliency-Aware Interpolative Augmentation for Multimodal Financial Prediction (2024.lrec-main)
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Samyak Jain, Parth Chhabra, Atula Tejaswi Neerkaje, Puneet Mathur, Ramit Sawhney, Shivam Agarwal, Preslav Nakov, Sudheer Chava, Dinesh Manocha
| Challenge: | Recent advances in the Financial AI realm have expanded the scope of data and methods they use, such as textual and audio cues from financial earnings calls, but limitations exist. |
| Approach: | They propose a Saliency-guided Hierarchical Mixup augmentation technique for multimodal financial prediction tasks. |
| Outcome: | The proposed technique outperforms state-of-the-art methods by 3-7% on financial earnings and conference call datasets. |
A Time-Aware Transformer Based Model for Suicide Ideation Detection on Social Media (2020.emnlp-main)
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| Challenge: | Suicide ideation is often linked to a history of mental depression. |
| Approach: | They propose a time-aware transformer based model for preliminary screening of suicidal risk on social media that augments linguistic models with historical context. |
| Outcome: | The proposed model outperforms competing models and shows that it is time-aware and contextually useful for suicide risk assessment. |
DocEdit-v2: Document Structure Editing Via Multimodal LLM Grounding (2024.emnlp-main)
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Manan Suri, Puneet Mathur, Franck Dernoncourt, Rajiv Jain, Vlad Morariu, Ramit Sawhney, Preslav Nakov, Dinesh Manocha
| Challenge: | Document structure editing involves manipulating localized textual, visual, and layout components in document images based on user’s requests. |
| Approach: | They propose a framework that performs end-to-end document editing by leveraging Large Multimodal Models (LMMs) by localizing edit regions of interest and disambiguating user edit requests into edit commands. |
| Outcome: | The proposed framework outperforms baselines on edit command generation (2-33%), RoI bounding box detection (12-31%), and overall document editing (1-12%) tasks. |
Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models (2022.naacl-main)
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| Challenge: | speculative trading of highly volatile assets such as cryptocurrencies and meme stocks presents a new challenge in the financial realm. |
| Approach: | They propose a multi-span bubble detection task based on social media hype and a set of sequence-to-sequence hyperbolic models . they use data from 9 exchanges over five years to test their models based upon the power-law dynamics of cryptocurrencies and user behavior on social networks. |
| Outcome: | The proposed model is able to detect bubbles on a set of reddit and twitter posts spanning over two million tweets over five years . |
RISE: Robust Early-exiting Internal Classifiers for Suicide Risk Evaluation (2024.lrec-main)
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| Challenge: | Existing systems for risk assessment are prone to incorrectly predicting risk severity and have no early detection mechanisms. |
| Approach: | They propose a novel mechanism for accurate early detection of suicide risk by ensembling Hyperbolic Internal Classifiers equipped with an abstention mechanism and early exit inference capabilities. |
| Outcome: | The proposed model abstains from 84% incorrect predictions on Reddit data while out-predicting state of the art models upto 3.5x earlier. |
Multitask Learning for Emotionally Analyzing Sexual Abuse Disclosures (2021.naacl-main)
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| Challenge: | Prior work on identifying narratives related to sexual abuse disclosures did not consider this as an independent task. |
| Approach: | They propose to identify narratives related to sexual abuse disclosures as a joint modeling task that leverages their emotional attributes through multitask learning. |
| Outcome: | The proposed model leverages emotional attributes of textual conversations to identify narratives related to sexual abuse disclosures in homogeneous and heterogeneously settings. |
#YouToo? Detection of Personal Recollections of Sexual Harassment on Social Media (P19-1)
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| Challenge: | a recent study has found that the disclosure of sexual abuse has positive psychological im- pacts. |
| Approach: | They propose to aggregate personal experiences of sexual harassment from Twitter posts to facilitate a better understanding of social media constructs and bring about social change. |
| Outcome: | The proposed model is compared with state-of-the-art models and is based on a three part Twitter-Specific Social Media Language Model. |
VolTAGE: Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls (2020.emnlp-main)
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| Challenge: | Existing approaches to stock volatility forecasting ignore correlations between stocks. |
| Approach: | They propose to combine vocal cues with verbal and financial cue data to create a multimodal stock volatility prediction model that accounts for stock interdependence via graph convolutions. |
| Outcome: | The proposed model outperforms existing methods showing that it can predict volatility using multimodal learning. |
Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning (2021.naacl-main)
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| Challenge: | Recent studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. |
| Approach: | They propose a framework leveraging a user’s emotional history and social information from a users neighborhood in a network to contextualize the interpretation of the latest tweet of a Twitter user. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on this task, showing the benefits of both socially and personally contextualized representations. |
Quantitative Day Trading from Natural Language using Reinforcement Learning (2021.naacl-main)
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| Challenge: | Existing approaches to stock prediction are not optimized to make profitable investment decisions. |
| Approach: | They propose a deep reinforcement learning approach that makes time-aware decisions to trade stocks while optimizing profit using textual data. |
| Outcome: | The proposed method outperforms state-of-the-art in terms of risk-adjusted returns on two benchmarks: Tweets (English) and financial news (Chinese) |
Augmenting NLP models using Latent Feature Interpolations (2020.coling-main)
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| Challenge: | Existing data augmentation methods with a large number of parameters are prone to over-fitting and often fail to capture the underlying input distribution. |
| Approach: | They propose a data augmentation technique that uses embeddings and hidden layer representations to construct virtual examples. |
| Outcome: | The proposed method outperforms existing methods in terms of accuracy and robustness to weight pruning. |
HYPHEN: Hyperbolic Hawkes Attention For Text Streams (2022.acl-short)
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| Challenge: | Existing methods for text stream modeling ignore fine-grained timing irregularities and time-varying scale-free properties of texts. |
| Approach: | They propose a hyperbolic Hawkes Attention Network which learns a data-driven hyperbolical space and models irregular powerlaw excitations using a Hawke's process. |
| Outcome: | The proposed model can model online text sequences in a geometry agnostic manner. |
Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment (N19-3)
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| Challenge: | #MeToo movement provides platform to narrate personal experiences of sexual harassment. |
| Approach: | They propose a three-part ULMFiT architecture to tackle text subtleties in a classification task . they propose to annotate a manually annotated real-world dataset to test their approach . |
| Outcome: | The proposed model outperforms existing models that rely on handcrafted stylistic features and is more accurate than generic models. |
AdaPT: A Set of Guidelines for Hyperbolic Multimodal Multilingual NLP (2024.findings-naacl)
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| Challenge: | Euclidean space is used for training neural models and performing arithmetic operations, but many data types have complex geometries and cannot be captured in the Euclidesan space. |
| Approach: | They propose a set of guidelines for initialization, parametrization, and training of neural networks that can be generalized over existing neural network training methodologies. |
| Outcome: | The proposed framework outperforms Euclidean methods on three tasks over 12 languages and modalities on a variety of domains. |
Multimodal Multi-Speaker Merger & Acquisition Financial Modeling: A New Task, Dataset, and Neural Baselines (2021.acl-long)
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| Challenge: | Merger and acquisition (M&A) calls provide key insights into claims made by company executives about restructuring of financial firms. |
| Approach: | They propose a baseline architecture that leverages multimodal multi-speaker input to forecast financial risk associated with M&A calls. |
| Outcome: | The proposed model performs marginally better than existing models based on BERT inputs . the proposed model is expected to be validated by the end of the year . |
PHASE: Learning Emotional Phase-aware Representations for Suicide Ideation Detection on Social Media (2021.eacl-main)
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| Challenge: | Recent studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. |
| Approach: | They propose a time-and-phase-aware framework that adaptively learns features from a user’s historical emotional spectrum to contextualize suicidal intent. |
| Outcome: | The proposed framework outperforms state-of-the-art methods while outperforming existing methods. |
FAST: Financial News and Tweet Based Time Aware Network for Stock Trading (2021.eacl-main)
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| Challenge: | Existing methods for stock movement prediction are limited and do not account for the fine-grain temporal irregularities in the release of large volumes of text. |
| Approach: | They propose a hierarchical, learning to rank approach that uses textual data to make time-aware predictions for ranking stocks based on expected profit. |
| Outcome: | The proposed method outperforms state-of-the-art methods by over 8% in terms of cumulative profit and risk-adjusted returns on two benchmarks: English tweets and Chinese financial news spanning two major stock indexes and four global markets. |
Tweet Based Reach Aware Temporal Attention Network for NFT Valuation (2022.findings-emnlp)
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Ramit Sawhney, Megh Thakkar, Ritesh Soun, Atula Neerkaje, Vasu Sharma, Dipanwita Guhathakurta, Sudheer Chava
| Challenge: | Non-Fungible Tokens (NFTs) are a relatively unexplored class of assets due to their extremely volatile nature. |
| Approach: | They propose a reach-aware temporal learning approach to predict future NFT trends from a dataset consisting of over 1.3 million tweets and 180 thousand NFT transactions . |
| Outcome: | The proposed model outperforms state-of-the-art models by an average of 36% on a dataset consisting of over 1.3 million tweets and 180 thousand NFT transactions spanning over 15 NFT collections. |
HypMix: Hyperbolic Interpolative Data Augmentation (2021.emnlp-main)
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| Challenge: | Existing methods for data augmentation involve performing mathematical operations over the raw input samples or their latent states representations, but these operations are performed in the Euclidean space, simplifying these representations and resulting in noisy interpolations. |
| Approach: | They propose a model-, data-, and modality-agnostic interpolative data augmentation technique operating in the hyperbolic space that captures the complex geometry of input and hidden state hierarchies better than its contemporaries. |
| Outcome: | The proposed technique outperforms state-of-the-art methods on benchmark and low resource datasets across speech, text, and vision modalities. |
Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations (2020.emnlp-main)
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| Challenge: | Existing models that predict stock movements are based on time series and technical analysis, but price signals alone fail to capture market surprises and impacts of sudden unexpected events. |
| Approach: | They propose a model that integrates chaotic temporal signals from financial data and social media to create hierarchical temporal networks. |
| Outcome: | The proposed model can be used to forecast stock movements on real-world S&P 500 index data and English tweets. |
DMix: Adaptive Distance-aware Interpolative Mixup (2022.acl-short)
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| Challenge: | Interpolation-based regularisation methods such as Mixup have shown to be effective for various tasks and modalities. |
| Approach: | They propose an adaptive distance-aware interpolative Mixup that selects samples based on their diversity in the embedding space. |
| Outcome: | The proposed method achieves state-of-the-art on sentence classification over existing methods on 8 benchmark datasets across English, Arabic, Turkish, and Hindi languages while achieving benchmark F1 scores in 3 times less number of iterations. |
A Computational Approach to Feature Extraction for Identification of Suicidal Ideation in Tweets (P18-3)
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| Challenge: | Suicidal ideation on social media websites is associated with higher suicide rates . suicide is the second leading cause of death among 15-29-year-olds . |
| Approach: | They propose a supervised method for detecting suicidal ideation in tweets using a dataset of manually annotated tweets. |
| Outcome: | The proposed method is compared against four baselines to validate its utility. |
DocFin: Multimodal Financial Prediction and Bias Mitigation using Semi-structured Documents (2022.findings-emnlp)
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Puneet Mathur, Mihir Goyal, Ramit Sawhney, Ritik Mathur, Jochen Leidner, Franck Dernoncourt, Dinesh Manocha
| Challenge: | Existing research focuses on textual and audio modalities of financial disclosures but ignores the rich tabular data available in financial reports. |
| Approach: | They propose to combine tabular financial data with text transcripts and audio recordings to improve stock volatility and price movement prediction by 5-12% and reduce gender bias by over 30%. |
| Outcome: | The combined data improves stock volatility and price movement prediction by 5-12% and reduces gender bias caused due to audio-based neural networks by over 30%. |
GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion (2020.coling-main)
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| Challenge: | Parliamentary debates are a valuable language resource for analyzing comprehensive options in a functional, free society. |
| Approach: | They propose a neural model for political speech sentiment analysis exploiting semantic representations and relations between debate transcripts, motions, and political party members. |
| Outcome: | The proposed model exploits semantic representations and relations between debate transcripts, motions, and political party members to predict political polarity and polarities. |
ARHNet - Leveraging Community Interaction for Detection of Religious Hate Speech in Arabic (P19-2)
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| Challenge: | Existing methods to detect hate speech in Arabic rely on textual cues and social network graphs. |
| Approach: | They propose to use Arabic word embeddings and social network graphs to profile hate speech in Arabic. |
| Outcome: | The proposed model incorporates Arabic Word Embeddings and Social Network Graphs for the detection of religious hate speech in Arabic. |