Papers by Revanth Gangi Reddy
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering (2021.findings-acl)
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Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramón Fernandez Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-Suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira Naseem, Sumit Neelam, Lucian Popa, Revanth Gangi Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu
| Challenge: | Existing approaches face challenges including complex question understanding and lack of large end-to-end training datasets. |
| Approach: | They propose a modular knowledge base question answering system that leverages AMR parses for task-independent question understanding. |
| Outcome: | The proposed system achieves state-of-the-art performance on two prominent KBQA datasets based on DBpedia. |
Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement (2025.coling-main)
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Chenkai Sun, Ke Yang, Revanth Gangi Reddy, Yi Fung, Hou Pong Chan, Kevin Small, ChengXiang Zhai, Heng Ji
| Challenge: | Existing research has focused on enhancing the retrieval stage and optimizing the representation of the database. |
| Approach: | They propose a framework to improve generalization across task contexts and collaborative refinement to bridge knowledge gaps among users. |
| Outcome: | The proposed framework improves generalization across task contexts and collaborative refinement to bridge knowledge gaps among users. |
NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge (2022.emnlp-main)
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Revanth Gangi Reddy, Sai Chetan Chinthakindi, Zhenhailong Wang, Yi Fung, Kathryn Conger, Ahmed ELsayed, Martha Palmer, Preslav Nakov, Eduard Hovy, Kevin Small, Heng Ji
| Challenge: | Current claims detection methods focus on sentence analysis, ignoring other attributes . a key element of identifying misinformation is detecting the claims and the arguments that have been presented. |
| Approach: | They propose a benchmark for attribute-aware claim detection in the news domain . they extend the problem to include extraction of additional attributes related to each claim . |
| Outcome: | The proposed system performs well on the test, but human performance is still poor. |
Towards Better Generalization in Open-Domain Question Answering by Mitigating Context Memorization (2024.findings-naacl)
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| Challenge: | Open-domain Question Answering (OpenQA) aims at answering factual questions using an external large-scale knowledge corpus. |
| Approach: | They propose a retrieval-augmented approach to QA that focuses on retrieving relevant knowledge from an external corpus. |
| Outcome: | The proposed model can generalize to completely different knowledge domains while adapting to updated versions of the same knowledge corpus and switching to completely new knowledge domain. |
Pushing the Limits of AMR Parsing with Self-Learning (2020.findings-emnlp)
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Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Revanth Gangi Reddy, Radu Florian, Salim Roukos
| Challenge: | Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years due to the impact of transfer learning and the development of novel architectures specific to AMR. |
| Approach: | They propose to use AMR annotations to generate synthetic text and refine actions oracle without additional human annotations for AMR parsing. |
| Outcome: | The proposed models improve on AMR 1.0 and 2.0 without human annotations. |
Answer Span Correction in Machine Reading Comprehension (2020.findings-emnlp)
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| Challenge: | Existing MRC systems produce only partially correct answers when presented with answerable questions. |
| Approach: | They propose a method that yields statistically significant performance improvements over existing MRC systems. |
| Outcome: | The proposed method yields statistically significant performance improvements over state-of-the-art MRC systems in monolingual and multilingual evaluation. |
Schema-Guided Culture-Aware Complex Event Simulation with Multi-Agent Role-Play (2024.emnlp-demo)
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Sha Li, Revanth Gangi Reddy, Khanh Nguyen, Qingyun Wang, Yi Fung, Chi Han, Jiawei Han, Kartik Natarajan, Clare Voss, Heng Ji
| Challenge: | Complex news events require swift responses from government and society, authors say . relying on historical events to project the future is insufficient, they say - a simulator for complex news events is needed . |
| Approach: | They propose a controllable complex news event simulator guided by event schema and user-provided assumptions . they incorporate a geo-diverse commonsense and cultural norm-aware knowledge enhancement component . |
| Outcome: | The proposed simulator achieves higher coherence and appropriateness than existing models. |
Multi-Level Memory for Task Oriented Dialogs (N19-1)
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| Challenge: | Recent task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. |
| Approach: | They propose a novel multi-level memory architecture that separates dialog context and knowledge base results . they use cells for each query and their corresponding results to address queries . |
| Outcome: | The proposed architecture outperforms current state-of-the-art models on three publicly available data sets. |
Infogent: An Agent-Based Framework for Web Information Aggregation (2025.findings-naacl)
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| Challenge: | Existing web navigation tasks evaluate web agents on task completion basis . however, information aggregation tasks have received relatively little attention . |
| Approach: | They propose a web navigation framework that uses three components for web information aggregation. |
| Outcome: | The proposed framework beats existing SOTA search framework by 7% under Direct API-Driven Access on FRAMES and improves over an existing information-seeking web agent by 4.3% under Interactive Visual Access on AssistantBench. |
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers (2024.findings-emnlp)
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Yuxia Wang, Revanth Gangi Reddy, Zain Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, Preslav Nakov
| Challenge: | Large language models generate naturally sounding answers over a broad range of human inquiries, but they often generate answers that contradict real-world facts. |
| Approach: | They propose a framework for annotating and evaluating the factuality of large language models . they propose 'factcheck-bench' which provides a multi-stage annotation scheme . |
| Outcome: | The proposed framework outperforms several popular LLM fact-checkers in claim, sentence, and document levels. |
FIRST: Faster Improved Listwise Reranking with Single Token Decoding (2024.emnlp-main)
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| Challenge: | Existing listwise LLMs lack efficiency as they provide ranking output in the form of a generated ordered sequence of candidate passage identifiers. |
| Approach: | They propose a listwise LLM reranking approach that leverages the first generated identifier to obtain a ranked ordering of the candidates. |
| Outcome: | The proposed approach accelerates inference by 50% while maintaining robust ranking performance with gains across BEIR benchmark. |
COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System (2022.acl-demo)
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Manling Li, Revanth Gangi Reddy, Ziqi Wang, Yi-shyuan Chiang, Tuan Lai, Pengfei Yu, Zixuan Zhang, Heng Ji
| Challenge: | a new system extracts supporting and refuting claims from COVID-19 related news . the system is publicly available at GitHub and DockerHub, with complete documentation. |
| Approach: | They propose a COVID-19 Claim Radar system that extracts supporting and refuting claims . the system leverages Wikidata as the hub to consolidate coreferential knowledge elements . |
| Outcome: | The system extracts supporting and refuting claims from COVID-19 pandemic information . it leverages Wikidata as the hub to merge coreferential knowledge elements . |
InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection (2021.acl-long)
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Yi Fung, Christopher Thomas, Revanth Gangi Reddy, Sandeep Polisetty, Heng Ji, Shih-Fu Chang, Kathleen McKeown, Mohit Bansal, Avi Sil
| Challenge: | a novel approach to detect fake news is needed due to training data scarcity . current methods focus on document-level fake news detection using lexical features and semantic embeddings . |
| Approach: | They propose a novel benchmark for fake news detection at the knowledge element level . they propose synthesis method which manipulates knowledge elements to generate noisy training data . |
| Outcome: | The proposed method outperforms the state-of-the-art in detecting misinformation . it yields fine-grained explanations and outperformed the current methods . |
A Zero-Shot Claim Detection Framework Using Question Answering (2022.coling-1)
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| Challenge: | Existing claims detection frameworks are portability to emerging events and low-resource training data settings. |
| Approach: | They propose a claim detection framework that leverages zero-shot Question Answering to solve sub-tasks such as topic filtering, claim object detection, and claimer detection. |
| Outcome: | The proposed framework outperforms baselines on the NewsClaims benchmark. |
Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning (2022.coling-1)
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Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan, Martin Franz, Vittorio Castelli, Heng Ji, Avirup Sil
| Challenge: | Existing neural IR systems rely on lexical matching for query-passage alignment, while masked language models use a dual encoder architecture to encode passages and questions into continuous vector representations. |
| Approach: | They propose to enhance the out-of-domain generalization of Dense Passage Retrieval (DPR) through synthetic data augmentation only in the source domain. |
| Outcome: | The proposed model outperforms existing models in in-domain and zero-shot evaluations on Wikipedia-based datasets. |
AGRaME: Any-Granularity Ranking with Multi-Vector Embeddings (2024.emnlp-main)
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| Challenge: | Existing ranking algorithms restrict granularity to full passages or require a specific dense index for each desired level of granules. |
| Approach: | They propose a multi-vector ranking approach that leverages multi-vctor embeddings to rank at varying levels of granularity while maintaining encoding at a single (coarser) level of grail. |
| Outcome: | The proposed method surpasses prompt-driven citation generation by incorporating proposition-level ranking to post-hoc citation addition. |
Multi-Stage Pre-training for Low-Resource Domain Adaptation (2020.emnlp-main)
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Rong Zhang, Revanth Gangi Reddy, Md Arafat Sultan, Vittorio Castelli, Anthony Ferritto, Radu Florian, Efsun Sarioglu Kayi, Salim Roukos, Avi Sil, Todd Ward
| Challenge: | Existing approaches to transfer learning target data to in-domain text . prior work has adapted pre-trained LMs to specific domains . |
| Approach: | They extend the vocabulary of a pretrained language model with domain-specific terms to create synthetic tasks that help it transfer to downstream tasks. |
| Outcome: | The proposed approaches show significant performance gains on extractive reading comprehension, document ranking and duplicate question detection tasks. |