no code implementations • COLING 2022 • Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan, Martin Franz, Vittorio Castelli, Heng Ji, Avirup Sil
Research on neural IR has so far been focused primarily on standard supervised learning settings, where it outperforms traditional term matching baselines.
1 code implementation • ACL 2022 • Manling Li, Revanth Gangi Reddy, Ziqi Wang, Yi-shyuan Chiang, Tuan Lai, Pengfei Yu, Zixuan Zhang, Heng Ji
To tackle the challenge of accurate and timely communication regarding the COVID-19 pandemic, we present a COVID-19 Claim Radar to automatically extract supporting and refuting claims on a daily basis.
no code implementations • COLING 2022 • Revanth Gangi Reddy, Sai Chetan Chinthakindi, Yi R. Fung, Kevin Small, Heng Ji
In recent years, there has been an increasing interest in claim detection as an important building block for misinformation detection.
no code implementations • 2 Apr 2024 • Zixuan Zhang, Revanth Gangi Reddy, Kevin Small, Tong Zhang, Heng Ji
In addition, it is still unclear how well an OpenQA model can transfer to completely new knowledge domains.
no code implementations • 16 Feb 2024 • Chenkai Sun, Ke Yang, Revanth Gangi Reddy, Yi R. Fung, Hou Pong Chan, ChengXiang Zhai, Heng Ji
The increasing demand for personalized interactions with large language models (LLMs) calls for the development of methodologies capable of accurately and efficiently identifying user opinions and preferences.
2 code implementations • 15 Nov 2023 • Yuxia Wang, Revanth Gangi Reddy, Zain Muhammad Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, Preslav Nakov
The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs.
no code implementations • 22 Oct 2023 • Revanth Gangi Reddy, Hao Bai, Wentao Yao, Sharath Chandra Etagi Suresh, Heng Ji, ChengXiang Zhai
Open-domain dialog involves generating search queries that help obtain relevant knowledge for holding informative conversations.
1 code implementation • 27 Jun 2023 • Liliang Ren, Mankeerat Sidhu, Qi Zeng, Revanth Gangi Reddy, Heng Ji, ChengXiang Zhai
Existing reference-free turn-level evaluation metrics for chatbots inadequately capture the interaction between the user and the system.
no code implementations • 19 May 2023 • Revanth Gangi Reddy, Pradeep Dasigi, Md Arafat Sultan, Arman Cohan, Avirup Sil, Heng Ji, Hannaneh Hajishirzi
Neural information retrieval often adopts a retrieve-and-rerank framework: a bi-encoder network first retrieves K (e. g., 100) candidates that are then re-ranked using a more powerful cross-encoder model to rank the better candidates higher.
1 code implementation • 25 Mar 2023 • Revanth Gangi Reddy, Yi R. Fung, Qi Zeng, Manling Li, Ziqi Wang, Paul Sullivan, Heng Ji
Further, experiments show that expert analysts tend to add more information into the SmartBook reports, with only 2. 3% of the existing tokens being deleted, meaning SmartBook can serve as a useful foundation for analysts to build upon when creating intelligence reports.
1 code implementation • 2 Dec 2022 • Revanth Gangi Reddy, Heba Elfardy, Hou Pong Chan, Kevin Small, Heng Ji
A primary objective of news articles is to establish the factual record for an event, frequently achieved by conveying both the details of the specified event (i. e., the 5 Ws; Who, What, Where, When and Why regarding the event) and how people reacted to it (i. e., reported statements).
1 code implementation • 24 Apr 2022 • Revanth Gangi Reddy, Md Arafat Sultan, Martin Franz, Avirup Sil, Heng Ji
On two public IR benchmarks, we empirically show that the proposed method helps improve both the model's attention patterns and retrieval performance, including in zero-shot settings.
no code implementations • 20 Apr 2022 • Revanth Gangi Reddy, Bhavani Iyer, Md Arafat Sultan, Rong Zhang, Avirup Sil, Vittorio Castelli, Radu Florian, Salim Roukos
Neural passage retrieval is a new and promising approach in open retrieval question answering.
2 code implementations • 20 Dec 2021 • Revanth Gangi Reddy, Xilin Rui, Manling Li, Xudong Lin, Haoyang Wen, Jaemin Cho, Lifu Huang, Mohit Bansal, Avirup Sil, Shih-Fu Chang, Alexander Schwing, Heng Ji
Specifically, the task involves multi-hop questions that require reasoning over image-caption pairs to identify the grounded visual object being referred to and then predicting a span from the news body text to answer the question.
2 code implementations • 16 Dec 2021 • Revanth Gangi Reddy, Sai Chetan, Zhenhailong Wang, Yi R. Fung, Kathryn Conger, Ahmed Elsayed, Martha Palmer, Preslav Nakov, Eduard Hovy, Kevin Small, Heng Ji
In this work, we present NewsClaims, a new benchmark for attribute-aware claim detection in the news domain.
no code implementations • ACL 2021 • Yi Fung, Christopher Thomas, Revanth Gangi Reddy, Sandeep Polisetty, Heng Ji, Shih-Fu Chang, Kathleen McKeown, Mohit Bansal, Avi Sil
To defend against machine-generated fake news, an effective mechanism is urgently needed.
no code implementations • 15 Apr 2021 • Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan, Martin Franz, Vittorio Castelli, Heng Ji, Avirup Sil
Recent work has shown that commonly available machine reading comprehension (MRC) datasets can be used to train high-performance neural information retrieval (IR) systems.
no code implementations • 2 Dec 2020 • Revanth Gangi Reddy, Bhavani Iyer, Md Arafat Sultan, Rong Zhang, Avi Sil, Vittorio Castelli, Radu Florian, Salim Roukos
End-to-end question answering (QA) requires both information retrieval (IR) over a large document collection and machine reading comprehension (MRC) on the retrieved passages.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Revanth Gangi Reddy, Md Arafat Sultan, Efsun Sarioglu Kayi, Rong Zhang, Vittorio Castelli, Avirup Sil
Answer validation in machine reading comprehension (MRC) consists of verifying an extracted answer against an input context and question pair.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Young-suk Lee, Ramon Fernandez Astudillo, Tahira Naseem, Revanth Gangi Reddy, Radu Florian, Salim Roukos
Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel architectures specific to AMR.
Ranked #2 on AMR Parsing on LDC2014T12
no code implementations • EMNLP 2020 • Rong Zhang, Revanth Gangi Reddy, Md Arafat Sultan, Vittorio Castelli, Anthony Ferritto, Radu Florian, Efsun Sarioglu Kayi, Salim Roukos, Avirup Sil, Todd Ward
Transfer learning techniques are particularly useful in NLP tasks where a sizable amount of high-quality annotated data is difficult to obtain.