Papers by Arpit Sharma
Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge (P19-1)
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| Challenge: | Existing methods to solve Winograd Schema Challenge use only knowledge embedded in text . this limits the performance of such models on the WSC problems. |
| Approach: | They propose to augment existing language models with a commonsense knowledge hunting module and an explicit reasoning module to extract the needed knowledge from text. |
| Outcome: | The proposed system improves on the language model based methods by 5.53% and 7.7% on the dataset. |
Bottom-Up Synthesis of Knowledge-Grounded Task-Oriented Dialogues with Iteratively Self-Refined Prompts (2025.naacl-short)
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| Challenge: | Training conversational question-answering systems requires in-domain data, which is often scarce in practice. |
| Approach: | They propose a bottom-up approach where QA pairs are generated first and combined into a coherent dialogue. |
| Outcome: | The proposed approach produces more realistic and higher-quality dialogues compared to top-down methods. |
Linguistically Motivated Features for Classifying Shorter Text into Fiction and Non-Fiction Genre (2022.coling-1)
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| Challenge: | linguistically motivated features are used to classify paragraph-level text into fiction and non-fiction genres. |
| Approach: | They deploy linguistically motivated features to classify paragraph-level text into fiction and non-fiction genres using a logistic regression model. |
| Outcome: | The proposed model gives 15.56% accuracy jump over baseline model . the proposed model also transfers over to another dataset, Baby BNC corpus . |