Papers by Naman Ahuja
Map&Make: Schema Guided Text to Table Generation (2025.acl-long)
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| Challenge: | Existing methods for text-to-table generation overlook what complex information to extract and how to infer it from text. |
| Approach: | They propose a method that decomposes text into atomic propositions to infer latent schemas. |
| Outcome: | The proposed method shows significant gains in accuracy and interpretability on three datasets. |
Moneyball with LLMs: Analyzing Tabular Summarization in Sports Narratives (2026.findings-acl)
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| Challenge: | Large language model (LLM) approaches to tabular summarization rely on prompt engineering, decomposition pipelines, or entity-level intermediate representations to achieve strong performance. |
| Approach: | They propose a diagnostic benchmark for long-context tabular summarization using decomposition pipelines and entity-level intermediate representations. |
| Outcome: | The proposed benchmark improves accuracy and numerical fidelity, but lacks local arithmetic. |
HashSet - A Dataset For Hashtag Segmentation (2022.lrec-1)
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| Challenge: | Hashtag segmentation is the task of breaking a hashtag into constituent tokens . hashtags are often written in unique ways, including spelling variations, and special characters. |
| Approach: | They propose a dataset that breaks hashtags into constituent tokens to train and validate models. |
| Outcome: | The proposed dataset provides an alternate set of hashtags to build and validate hashtag segmentation models. |