Papers by Siddharth Jain
A Large-scale Evaluation of Neural Machine Transliteration for Indic Languages (2021.eacl-main)
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| Challenge: | We analyze multilingual transliteration for Indic languages using scripts derived from the ancient Brahmi script. |
| Approach: | They propose a multilingual training recipe for Indic languages that utilizes orthographic similarity between English and Indic. |
| Outcome: | The proposed training recipe improves multilingual transliteration for Indic languages. |
Learning to Speak and Act in a Fantasy Text Adventure Game (D19-1)
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Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, Jason Weston
| Challenge: | Existing studies on grounded dialogue use only statistical regularities of text data, without explicit understanding of the world that the text describes. |
| Approach: | They propose a large-scale crowdsourced text adventure game as a research platform for studying grounded dialogue. |
| Outcome: | The proposed game allows agents to perceive, emote, and act whilst conducting dialogue with other agents. |
InfoSync: Information Synchronization across Multilingual Semi-structured Tables (2023.findings-acl)
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| Challenge: | Information Synchronization of semi-structured data across languages is challenging . culture differences, topic preferences, and editing inconsistency lead to information mismatches . |
| Approach: | They propose a method for tabular synchronization that uses information from Wikipedia tables in one language with tables in another language. |
| Outcome: | The proposed method achieves an acceptance rate of 77.28% on Wikipedia . english articles across the web are more timely updated than other languages . |
Bridging the Creativity Understanding Gap: Small-Scale Human Alignment Enables Expert-Level Humor Ranking in LLMs (2025.findings-emnlp)
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Kuan Lok Zhou, Jiayi Chen, Siddharth Suresh, Reuben Narad, Timothy T. Rogers, Lalit K Jain, Robert D Nowak, Bob Mankoff, Jifan Zhang
| Challenge: | Large Language Models (LLMs) have shown significant limitations in understanding creative content, as demonstrated by Hessel et al. (2023)’s influential work on the New Yorker Cartoon Caption Contest. |
| Approach: | They propose to decompose humor understanding into three components and improve each by enhancing visual understanding through improved annotation and utilizing LLM-generated humor reasoning and explanations. |
| Outcome: | The proposed approach achieves 82.4% accuracy in caption ranking, significantly better than the previous 67% benchmark and matches the performance of world-renowned human experts in this domain. |