Papers by Tanmay Parekh
Contextual Label Projection for Cross-Lingual Structured Prediction (2024.naacl-long)
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| Challenge: | Prior work favors simplified label translation or relying on word-level alignments for label projection. |
| Approach: | They propose a novel approach CLaP which translates text to target language and performs *contextual translation* on the labels using the translated text as the context. |
| Outcome: | The proposed approach improves translation accuracy on two prediction tasks and shows 2.4 F1 improvement for EAE and 1.4 F1 for named entity recognition. |
Code-switched Language Models Using Dual RNNs and Same-Source Pretraining (D18-1)
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| Challenge: | Using recurrent neural networks to build language models for code-switched text is an important problem with implications to downstream applications such as speech recognition and machine translation. |
| Approach: | They propose a novel recurrent neural network unit with dual components that focus on each language in the code-switched text separately and a generative model estimated using the training data. |
| Outcome: | The proposed techniques yield significant reductions in perplexity on Mandarin-English task and improve on baseline models. |
SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness (2024.emnlp-main)
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Tanmay Parekh, Jeffrey Kwan, Jiarui Yu, Sparsh Johri, Hyosang Ahn, Sreya Muppalla, Kai-Wei Chang, Wei Wang, Nanyun Peng
| Challenge: | Prior studies focused on English posts to provide early warnings for epidemic prediction, but these work focused on non-English posts. |
| Approach: | They propose a multilingual event extraction framework for extracting epidemic event information for any disease and language using 5.1K tweets in four languages. |
| Outcome: | The proposed framework can provide epidemic warnings for COVID-19 in its earliest stages in Dec 2019 (3 weeks before global discussions) and aggregate community epidemic discussions like symptoms and cure measures, aiding misinformation detection and public attention monitoring. |
SNaRe: Domain-aware Data Generation for Low-Resource Event Detection (2025.emnlp-main)
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| Challenge: | Existing methods for ED struggle with label noise and domain drift when applied to specialized domains. |
| Approach: | They propose a domain-aware synthetic data generation framework composed of three components: Scout, Narrator, and Refiner. |
| Outcome: | The proposed framework outperforms baseline approaches on three diverse domain ED datasets and achieves average F1 gains of 3-7% in the zero-shot/few-shot settings and 4-20% improvement for multilingual generation. |
DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning (2025.emnlp-main)
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| Challenge: | Understanding the complex event ontology, extracting domain-specific triggers from the passage, and structuring them appropriately overloads and limits the utility of Large Language Models (LLMs). |
| Approach: | They propose a divergent-convergent reasoning framework that decouples the task of ED using Dreamer and Grounder. |
| Outcome: | The proposed framework outperforms baselines on six datasets across five domains and nine LLMs, achieving 4–7% average gains over the best baseline. |
GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles (2023.acl-long)
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| Challenge: | Existing benchmarking datasets for Event Argument Extraction (EAE) cover less than 40 event types and 25 entity-centric argument roles. |
| Approach: | They propose to use a large and diverse EAE ontology to create a semantic role labeling dataset for EAE that incorporates 115 events and 220 argument roles. |
| Outcome: | The proposed ontology concludes with 115 events and 220 argument roles, with a significant portion of roles not being entities. |
PExA: Parallel Exploration Agent for Complex Text-to-SQL (2026.acl-short)
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| Challenge: | Recent work in text-to-SQL has explored toolaugmented LLMs, deep planning, and agentic workflows to address complex challenges. |
| Approach: | They validated a framework for text-to-SQL, Spider 2.0, with 70.2% execution accuracy. |
| Outcome: | The proposed framework achieves 70.2% execution accuracy on a state-of-the-art benchmark for text-to-SQL, Spider 2.0. |
QUDSELECT: Selective Decoding for Questions Under Discussion Parsing (2024.emnlp-main)
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| Challenge: | Question Under Discussion (QUD) uses implicit questions to reveal discourse relationships between sentences. |
| Approach: | They propose a framework that selectively decodes the QUD dependency structures considering the QUC criteria. |
| Outcome: | The proposed framework outperforms the state-of-the-art baseline models by 9% in human evaluation and 4% in automatic evaluation. |
Dynamic Strategy Planning for Efficient Question Answering with Large Language Models (2025.findings-naacl)
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| Challenge: | Existing studies have shown that using a single fixed strategy for answering all different kinds of questions is sub-optimal in performance and inefficient in terms of generated tokens and retrievals. |
| Approach: | They propose a technique to induce a dynamic strategy selection process in Large Language Models (LLMs) by incorporating an initial decision step to select the most suitable strategy conditioned on the input question and guides the LLM’s response generation accordingly. |
| Outcome: | The proposed technique improves model performance by 7-13% while reducing the cost by 11-32% relative to the best baseline model. |
TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction (2024.findings-acl)
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Kuan-Hao Huang, I-Hung Hsu, Tanmay Parekh, Zhiyu Xie, Zixuan Zhang, Prem Natarajan, Kai-Wei Chang, Nanyun Peng, Heng Ji
| Challenge: | Recent studies suggest that event extraction evaluations may not accurately reflect the true performance. |
| Approach: | They propose a standardized, fair, and reproducible benchmark for event extraction . they use standardized scripts and splits for 16 datasets spanning eight domains . |
| Outcome: | The proposed benchmarks show that they struggle to achieve satisfactory performance. |
Politeness Transfer: A Tag and Generate Approach (2020.acl-main)
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Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W Black, Shrimai Prabhumoye
| Challenge: | Prior work on text style transfer has not focused on politeness as a style transfer task and we argue that defining it is cumbersome. |
| Approach: | They propose a task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. |
| Outcome: | The proposed model outperforms state-of-the-art methods on content preservation and style transfer accuracy. |
Event Detection from Social Media for Epidemic Prediction (2024.naacl-long)
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Tanmay Parekh, Anh Mac, Jiarui Yu, Yuxuan Dong, Syed Shahriar, Bonnie Liu, Eric Yang, Kuan-Hao Huang, Wei Wang, Nanyun Peng, Kai-Wei Chang
| Challenge: | Social media is an easy-to-access platform providing timely updates about societal trends and events. |
| Approach: | They propose a framework to extract epidemic-related events from social media posts to provide early warnings. |
| Outcome: | The proposed framework can detect epidemic events for three unseen epidemics of Monkeypox, Zika, and Dengue while existing models fail miserably. |