Papers by Anirudh Srinivasan
To Answer or Not to Answer (TAONA): A Robust Textual Graph Understanding and Question Answering Approach (2025.findings-emnlp)
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Yuchen Yan, Aakash Kolekar, Sahika Genc, Wenju Xu, Edward W Huang, Anirudh Srinivasan, Mukesh Jain, Qi He, Hanghang Tong
| Challenge: | Existing studies assume that generated answers integrate all relevant information from the textual graph. |
| Approach: | They propose a novel GraphRAG model that integrates all relevant information from the textual graph into the generated answer. |
| Outcome: | Extensive experiments validate TAONA’s superior performance for both A-side and B-side tasks. |
Peering into the Mind of Language Models: An Approach for Attribution in Contextual Question Answering (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly used for question answering . lack of explicit references or attributions hinders ability to verify accuracy of answers . |
| Approach: | They propose a method for attribution in contextual question answering . they use hidden state representations of large language models to identify copied segments . |
| Outcome: | The proposed method performs better than GPT-4 at identifying verbatim copied segments in LLM generations and attributing these segments to their source. |
TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages (2022.findings-emnlp)
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| Challenge: | Whether politeness phenomena and strategies are universal across languages or not have been controversial among sociologists and linguists. |
| Approach: | They create a dataset containing three-way politeness annotations for 500 examples in each language, totaling 4.5K examples. |
| Outcome: | The proposed model shows a robust zero-shot transfer ability, but falls short of estimated human accuracy significantly. |
GLUECoS: An Evaluation Benchmark for Code-Switched NLP (2020.acl-main)
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| Challenge: | Recent studies show multilingual contextual embedding models perform better on cross-lingual and multilingual tasks. |
| Approach: | They propose to evaluate multilingual contextual embedding models on multilingual data . they use language identification from text, POS tagging, Named Entity Recognition and Question Answering . |
| Outcome: | The proposed benchmark evaluates models on language identification from text, POS tagging, Named Entity Recognition, Question Answering and a new task for code-switching, Natural Language Inference. |
Textless Speech-to-Speech Translation With Limited Parallel Data (2024.findings-emnlp)
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| Challenge: | Existing speech-to-speech translation models either leverage text as an intermediate step or require hundreds of hours of parallel speech data. |
| Approach: | They propose a framework for training textless S2ST models that require dozens of hours of parallel speech data. |
| Outcome: | The proposed model achieves reasonable performance on three domains with single-speaker synthesized speech. |
Processing and Understanding Mixed Language Data (D19-2)
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| Challenge: | Multilingual communities exhibit code-mixing, mixing of two or more languages in a single conversation . social media and other informal interactive platforms are allowing code-switching in user-generated text . |
| Approach: | a tutorial aims to provide a foundation for researchers to study code-mixing in multilingual communities. |
| Outcome: | a tutorial aims to provide new researchers with a foundation in linguistics and computational aspects of code-mixing. |
GCM: A Toolkit for Generating Synthetic Code-mixed Text (2021.eacl-demos)
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| Challenge: | Code-mixing is a spoken language phenomenon and is difficult to train in multilingual communities. |
| Approach: | They propose a tool that can automatically generate code-mixed data given parallel data in two languages. |
| Outcome: | The proposed tool can generate code-mixed data in two languages using two linguistic theories. |