Papers by Shivam Agarwal
DynaMiTE: Discovering Explosive Topic Evolutions with User Guidance (2023.findings-acl)
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| Challenge: | Existing Dynamic topic models are either fully supervised, requiring expensive human annotations, or fully unsupervised, producing topic evolutions that often do not cater to a user’s needs. |
| Approach: | They propose to use a framework that ensembles semantic similarity, category indicative, and time indicative scores to produce informative topic evolutions. |
| Outcome: | The proposed framework can be used to discover topic evolutions from temporal corpora that align with user-provided category names and uniquely capture topics at each time step. |
Text Augmented Open Knowledge Graph Completion via Pre-Trained Language Models (2023.findings-acl)
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| Challenge: | Existing methods to augment knowledge graph completion require factual triples or manual prompts to extract knowledge from a pre-trained language model. |
| Approach: | They propose a tool that generates quality query prompts and retrieves support information from large text corpora to probe knowledge from a pre-trained language model. |
| Outcome: | The proposed method outperforms embedding-based, graph-based and PLM-based methods on two benchmark datasets. |
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. |
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 . |
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) |
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. |
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. |
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. |
Nanda Family: Open-Weights Generative Large Language Models for Hindi (2026.eacl-long)
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Aaryamonvikram Singh, Debopriyo Banerjee, Dhruv Sahnan, Monojit Choudhury, Shivam Chauhan, Rocktim Jyoti Das, Xudong Han, Haonan Li, Alok Anil Jadhav, Utkarsh Agarwal, Mukund Choudhary, Fajri Koto, Junaid Hamid Bhat, Awantika Shukla, Samujjwal Ghosh, Samta Kamboj, Onkar Pandit, Lalit Pradhan, Rahul Pal, Sunil Kumar Sahu, Parvez Mullah, Ali El Filali, Zainul Abedien Ahmed Quraishi, Neha Sengupta, Gokulakrishnan Ramakrishnan, Rituraj Joshi, Gurpreet Gosal, Avraham Sheinin, Natalia Vassilieva, Preslav Nakov
| Challenge: | Large language models remain predominantly English-centric, which limits their utility for underrepresented languages. |
| Approach: | They propose to extend Llama’s vocabulary with 20% Hindi-specific tokens, thus halving Hindi tokenization fertility while preserving English efficiency. |
| Outcome: | The proposed models outperform open-weight models of comparable size on a 65B-token corpus and bilingual instruction and safety alignment on . a culturally grounded dataset. |
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. |
MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing (2024.findings-acl)
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| Challenge: | Recent studies have focused on harms of memes in closed environments, such as hate speech and cyber-bullying. |
| Approach: | They propose a multimodal question-answering framework that solicits accurate responses to structured questions while providing coherent explanations. |
| Outcome: | The proposed framework outperforms existing frameworks in predicting answer prediction accuracy and text generation lead over a baseline. |