no code implementations • BigScience (ACL) 2022 • Zeerak Talat, Aurélie Névéol, Stella Biderman, Miruna Clinciu, Manan Dey, Shayne Longpre, Sasha Luccioni, Maraim Masoud, Margaret Mitchell, Dragomir Radev, Shanya Sharma, Arjun Subramonian, Jaesung Tae, Samson Tan, Deepak Tunuguntla, Oskar van der Wal
Evaluating bias, fairness, and social impact in monolingual language models is a difficult task.
no code implementations • ACL (RepL4NLP) 2021 • Irene Li, Prithviraj Sen, Huaiyu Zhu, Yunyao Li, Dragomir Radev
In this paper, we propose zero-shot instance-weighting, a general model-agnostic zero-shot learning framework for improving CLTC by leveraging source instance weighting.
1 code implementation • Findings (EMNLP) 2021 • Yusen Zhang, Ansong Ni, Tao Yu, Rui Zhang, Chenguang Zhu, Budhaditya Deb, Asli Celikyilmaz, Ahmed Hassan Awadallah, Dragomir Radev
Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series.
no code implementations • 25 May 2022 • Linyong Nan, Lorenzo Jaime Yu Flores, Yilun Zhao, Yixin Liu, Luke Benson, Weijin Zou, Dragomir Radev
Unfaithful text generation is a common problem for text generation systems.
no code implementations • 25 May 2022 • Yixin Liu, Ansong Ni, Linyong Nan, Budhaditya Deb, Chenguang Zhu, Ahmed H. Awadallah, Dragomir Radev
Our experimental results show that our model can have better performance compared with strong baseline models with efficient attention modules, and our analysis provides further insights of our locality-aware modeling strategy.
1 code implementation • 19 May 2022 • Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Hao Peng, Ximing Lu, Dragomir Radev, Yejin Choi, Noah A. Smith
Natural language generation technology has recently seen remarkable progress with large-scale training, and many natural language applications are now built upon a wide range of generation models.
1 code implementation • 15 May 2022 • Fang Wu, Siyuan Li, Lirong Wu, Dragomir Radev, Qiang Zhang, Stan Z. Li
To investigate the underlying mechanism, we explore the capacity of GNNs to capture pairwise interactions between nodes under contexts with different complexities, especially for their graph-level and node-level applications in scientific domains like biochemistry and physics.
1 code implementation • 13 Apr 2022 • Irene Li, Keen You, Xiangru Tang, Yujie Qiao, Lucas Huang, Chia-Chun Hsieh, Benjamin Rosand, Dragomir Radev
The Electronic Health Record (EHR) is an essential part of the modern medical system and impacts healthcare delivery, operations, and research.
1 code implementation • 11 Apr 2022 • Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Dragomir Radev, Yejin Choi, Noah A. Smith
Text generation with beam search has proven successful in a wide range of applications.
1 code implementation • ACL 2022 • Yixin Liu, PengFei Liu, Dragomir Radev, Graham Neubig
Abstractive summarization models are commonly trained using maximum likelihood estimation, which assumes a deterministic (one-point) target distribution in which an ideal model will assign all the probability mass to the reference summary.
Ranked #1 on
Abstractive Text Summarization
on CNN / Daily Mail
1 code implementation • ACL 2022 • Stephen H. Bach, Victor Sanh, Zheng-Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-David, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Alan Fries, Maged S. Al-shaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Dragomir Radev, Mike Tian-Jian Jiang, Alexander M. Rush
PromptSource is a system for creating, sharing, and using natural language prompts.
1 code implementation • 16 Jan 2022 • Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.
Ranked #1 on
Task-Oriented Dialogue Systems
on KVRET
no code implementations • 7 Jan 2022 • Irene Li, Thomas George, Alexander Fabbri, Tammy Liao, Benjamin Chen, Rina Kawamura, Richard Zhou, Vanessa Yan, Swapnil Hingmire, Dragomir Radev
In this paper, we propose the educational resource discovery (ERD) pipeline that automates web resource discovery for novel domains.
no code implementations • 16 Dec 2021 • Xiangru Tang, Arjun Nair, Borui Wang, Bingyao Wang, Jai Desai, Aaron Wade, Haoran Li, Asli Celikyilmaz, Yashar Mehdad, Dragomir Radev
Using human evaluation and automatic faithfulness metrics, we show that our model significantly reduces all kinds of factual errors on the dialogue summarization, SAMSum corpus.
no code implementations • 16 Dec 2021 • Swapnil Hingmire, Irene Li, Rena Kawamura, Benjamin Chen, Alexander Fabbri, Xiangru Tang, Yixin Liu, Thomas George, Tammy Liao, Wai Pan Wong, Vanessa Yan, Richard Zhou, Girish K. Palshikar, Dragomir Radev
We propose a classification scheme -- CLICKER for CL/NLP based on the analysis of online lectures from 77 university courses on this subject.
no code implementations • 13 Dec 2021 • Irene Li, Alexander Fabbri, Rina Kawamura, Yixin Liu, Xiangru Tang, Jaesung Tae, Chang Shen, Sally Ma, Tomoe Mizutani, Dragomir Radev
Fast-developing fields such as Artificial Intelligence (AI) often outpace the efforts of encyclopedic sources such as Wikipedia, which either do not completely cover recently-introduced topics or lack such content entirely.
2 code implementations • ACL 2022 • Yusen Zhang, Ansong Ni, Ziming Mao, Chen Henry Wu, Chenguang Zhu, Budhaditya Deb, Ahmed H. Awadallah, Dragomir Radev, Rui Zhang
To the best of our knowledge, Summ$^N$ is the first multi-stage split-then-summarize framework for long input summarization.
1 code implementation • ACL 2022 • Ziming Mao, Chen Henry Wu, Ansong Ni, Yusen Zhang, Rui Zhang, Tao Yu, Budhaditya Deb, Chenguang Zhu, Ahmed H. Awadallah, Dragomir Radev
Transformer-based models have achieved state-of-the-art performance on short-input summarization.
2 code implementations • 4 Oct 2021 • Fang Wu, Qiang Zhang, Dragomir Radev, Jiyu Cui, Wen Zhang, Huabin Xing, Ningyu Zhang, Huajun Chen
To address such issues, we formulate heterogeneous molecular graphs (HMGs), and introduce Molformer to exploit both molecular motifs and 3D geometry.
no code implementations • 19 Sep 2021 • Xiangru Tang, Alexander R. Fabbri, Ziming Mao, Griffin Adams, Borui Wang, Haoran Li, Yashar Mehdad, Dragomir Radev
Current pre-trained models applied to summarization are prone to factual inconsistencies which either misrepresent the source text or introduce extraneous information.
no code implementations • 17 Sep 2021 • Irene Li, Vanessa Yan, Dragomir Radev
Our novel model consists of a variational graph autoencoder (VGAE) and a domain discriminator.
1 code implementation • 10 Sep 2021 • Yusen Zhang, Ansong Ni, Tao Yu, Rui Zhang, Chenguang Zhu, Budhaditya Deb, Asli Celikyilmaz, Ahmed Hassan Awadallah, Dragomir Radev
Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series.
1 code implementation • EMNLP (ACL) 2021 • Ansong Ni, Zhangir Azerbayev, Mutethia Mutuma, Troy Feng, Yusen Zhang, Tao Yu, Ahmed Hassan Awadallah, Dragomir Radev
We also provide explanations for models and evaluation metrics to help users understand the model behaviors and select models that best suit their needs.
no code implementations • 7 Jul 2021 • Irene Li, Jessica Pan, Jeremy Goldwasser, Neha Verma, Wai Pan Wong, Muhammed Yavuz Nuzumlali, Benjamin Rosand, Yixin Li, Matthew Zhang, David Chang, R. Andrew Taylor, Harlan M. Krumholz, Dragomir Radev
Electronic health records (EHRs), digital collections of patient healthcare events and observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and research.
1 code implementation • Findings (ACL) 2021 • Wenpeng Yin, Dragomir Radev, Caiming Xiong
It has been studied intensively in the past few years thanks to the availability of large-scale labeled datasets.
1 code implementation • ACL 2021 • Alexander R. Fabbri, Faiaz Rahman, Imad Rizvi, Borui Wang, Haoran Li, Yashar Mehdad, Dragomir Radev
While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles.
1 code implementation • 18 May 2021 • Wojciech Kryściński, Nazneen Rajani, Divyansh Agarwal, Caiming Xiong, Dragomir Radev
The majority of available text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and stylistic biases.
no code implementations • ACL 2021 • Irene Li, Vanessa Yan, Tianxiao Li, Rihao Qu, Dragomir Radev
For example, one may be an expert in the natural language processing (NLP) domain but want to determine the best order to learn new concepts in an unfamiliar Computer Vision domain (CV).
1 code implementation • NAACL 2021 • Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed Hassan Awadallah, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, Dragomir Radev
As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed.
1 code implementation • 1 Apr 2021 • Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Nick Schoelkopf, Riley Kong, Xiangru Tang, Murori Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, Dragomir Radev
Existing table question answering datasets contain abundant factual questions that primarily evaluate the query and schema comprehension capability of a system, but they fail to include questions that require complex reasoning and integration of information due to the constraint of the associated short-form answers.
no code implementations • NAACL 2021 • Alexander R. Fabbri, Simeng Han, Haoyuan Li, Haoran Li, Marjan Ghazvininejad, Shafiq Joty, Dragomir Radev, Yashar Mehdad
Models pretrained with self-supervised objectives on large text corpora achieve state-of-the-art performance on English text summarization tasks.
1 code implementation • EMNLP 2020 • Wenpeng Yin, Nazneen Fatema Rajani, Dragomir Radev, Richard Socher, Caiming Xiong
We demonstrate that this framework enables a pretrained entailment model to work well on new entailment domains in a few-shot setting, and show its effectiveness as a unified solver for several downstream NLP tasks such as question answering and coreference resolution when the end-task annotations are limited.
1 code implementation • ICLR 2021 • Tao Yu, Chien-Sheng Wu, Xi Victoria Lin, Bailin Wang, Yi Chern Tan, Xinyi Yang, Dragomir Radev, Richard Socher, Caiming Xiong
We present GraPPa, an effective pre-training approach for table semantic parsing that learns a compositional inductive bias in the joint representations of textual and tabular data.
Ranked #4 on
Semantic Parsing
on spider
5 code implementations • 24 Jul 2020 • Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, Dragomir Radev
The scarcity of comprehensive up-to-date studies on evaluation metrics for text summarization and the lack of consensus regarding evaluation protocols continue to inhibit progress.
2 code implementations • NAACL 2021 • Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani
Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures.
no code implementations • 17 Jun 2020 • Andre Esteva, Anuprit Kale, Romain Paulus, Kazuma Hashimoto, Wenpeng Yin, Dragomir Radev, Richard Socher
The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines.
2 code implementations • ACL 2020 • Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan, Stephan Zheng, Jeremy Weiss, Aadit Vyas, Abhijit Gupta, Caiming Xiong, Richard Socher, Dragomir Radev
Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions.
1 code implementation • COLING 2020 • Irene Li, Alexander Fabbri, Swapnil Hingmire, Dragomir Radev
The task of concept prerequisite chain learning is to automatically determine the existence of prerequisite relationships among concept pairs.
1 code implementation • 30 Oct 2019 • Irene Li, Michihiro Yasunaga, Muhammed Yavuz Nuzumlali, Cesar Caraballo, Shiwani Mahajan, Harlan Krumholz, Dragomir Radev
Specifically, a neural topic-attention model is applied to learn improved contextualized sentence representations for medical term abbreviation disambiguation.
4 code implementations • IJCNLP 2019 • Tao Yu, Rui Zhang, He Yang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter S. Lasecki, Dragomir Radev
We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems.
2 code implementations • IJCNLP 2019 • Rui Zhang, Tao Yu, He Yang Er, Sungrok Shim, Eric Xue, Xi Victoria Lin, Tianze Shi, Caiming Xiong, Richard Socher, Dragomir Radev
We focus on the cross-domain context-dependent text-to-SQL generation task.
1 code implementation • 2 Sep 2019 • Kokil Jaidka, Michihiro Yasunaga, Muthu Kumar Chandrasekaran, Dragomir Radev, Min-Yen Kan
This overview describes the official results of the CL-SciSumm Shared Task 2018 -- the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain.
1 code implementation • 23 Jul 2019 • Muthu Kumar Chandrasekaran, Michihiro Yasunaga, Dragomir Radev, Dayne Freitag, Min-Yen Kan
All papers are from the open access research papers in the CL domain.
2 code implementations • 26 Jun 2019 • Youngnam Lee, Youngduck Choi, Junghyun Cho, Alexander R. Fabbri, HyunBin Loh, Chanyou Hwang, Yongku Lee, Sang-Wook Kim, Dragomir Radev
Our model outperforms existing approaches over several metrics in predicting user response correctness, notably out-performing other methods on new users without large question-response histories.
no code implementations • ACL 2019 • Rui Zhang, Caitlin Westerfield, Sungrok Shim, Garrett Bingham, Alexander Fabbri, Neha Verma, William Hu, Dragomir Radev
In this paper, we propose to boost low-resource cross-lingual document retrieval performance with deep bilingual query-document representations.
5 code implementations • ACL 2019 • Tao Yu, Rui Zhang, Michihiro Yasunaga, Yi Chern Tan, Xi Victoria Lin, Suyi Li, Heyang Er, Irene Li, Bo Pang, Tao Chen, Emily Ji, Shreya Dixit, David Proctor, Sungrok Shim, Jonathan Kraft, Vincent Zhang, Caiming Xiong, Richard Socher, Dragomir Radev
The best model obtains an exact match accuracy of 20. 2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research.
1 code implementation • NAACL 2019 • Jungo Kasai, Dan Friedman, Robert Frank, Dragomir Radev, Owen Rambow
We introduce a new syntax-aware model for dependency-based semantic role labeling that outperforms syntax-agnostic models for English and Spanish.
2 code implementations • 11 Oct 2018 • Tao Yu, Michihiro Yasunaga, Kai Yang, Rui Zhang, Dongxu Wang, Zifan Li, Dragomir Radev
In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task.
no code implementations • EMNLP 2018 • Tao Yu, Michihiro Yasunaga, Kai Yang, Rui Zhang, Dongxu Wang, Zifan Li, Dragomir Radev
In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task.
4 code implementations • EMNLP 2018 • Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, Dragomir Radev
We define a new complex and cross-domain semantic parsing and text-to-SQL task where different complex SQL queries and databases appear in train and test sets.
Ranked #6 on
Semantic Parsing
on spider
1 code implementation • ACL 2018 • Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, Dragomir Radev
Second, we show that the current division of data into training and test sets measures robustness to variations in the way questions are asked, but only partially tests how well systems generalize to new queries; therefore, we propose a complementary dataset split for evaluation of future work.
Ranked #1 on
SQL Parsing
on ATIS
no code implementations • ACL 2018 • Rui Zhang, Cicero Nogueira dos santos, Michihiro Yasunaga, Bing Xiang, Dragomir Radev
Coreference resolution aims to identify in a text all mentions that refer to the same real-world entity.
no code implementations • NAACL 2018 • Tao Yu, Zifan Li, Zilin Zhang, Rui Zhang, Dragomir Radev
Interacting with relational databases through natural language helps users of any background easily query and analyze a vast amount of data.
Ranked #2 on
Code Generation
on WikiSQL
1 code implementation • NAACL 2018 • Michihiro Yasunaga, Jungo Kasai, Dragomir Radev
Adversarial training (AT) is a powerful regularization method for neural networks, aiming to achieve robustness to input perturbations.
Ranked #2 on
Part-Of-Speech Tagging
on UD
1 code implementation • 12 Sep 2017 • Rui Zhang, Honglak Lee, Lazaros Polymenakos, Dragomir Radev
In this paper, we study the problem of addressee and response selection in multi-party conversations.
no code implementations • CONLL 2017 • Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, Dragomir Radev
We propose a neural multi-document summarization (MDS) system that incorporates sentence relation graphs.
Ranked #1 on
Multi-Document Summarization
on DUC 2004
2 code implementations • NAACL 2016 • Rui Zhang, Honglak Lee, Dragomir Radev
Moreover, unlike other CNN-based models that analyze sentences locally by sliding windows, our system captures both the dependency information within each sentence and relationships across sentences in the same document.
2 code implementations • 8 Nov 2016 • Lajanugen Logeswaran, Honglak Lee, Dragomir Radev
Modeling the structure of coherent texts is a key NLP problem.
no code implementations • 8 Nov 2016 • Dragomir Radev, Rui Zhang, Steve Wilson, Derek Van Assche, Henrique Spyra Gubert, Alisa Krivokapic, MeiXing Dong, Chongruo wu, Spruce Bondera, Luke Brandl, Jeremy Dohmann
We describe the results of several of our experiments with the system.
no code implementations • LREC 2016 • {\c{S}}aziye Bet{\"u}l {\"O}zate{\c{s}}, Arzucan {\"O}zg{\"u}r, Dragomir Radev
We introduce an approach based on using the dependency grammar representations of sentences to compute sentence similarity for extractive multi-document summarization.
no code implementations • LREC 2016 • Yashar Mehdad, Am Stent, a, Kapil Thadani, Dragomir Radev, Youssef Billawala, Karolina Buchner
In this paper we report a comparison of various techniques for single-document extractive summarization under strict length budgets, which is a common commercial use case (e. g. summarization of news articles by news aggregators).
no code implementations • 25 Mar 2016 • Reed Coke, Ben King, Dragomir Radev
This paper presents a comparison of classification methods for linguistic typology for the purpose of expanding an extensive, but sparse language resource: the World Atlas of Language Structures (WALS) (Dryer and Haspelmath, 2013).
no code implementations • LREC 2016 • Dragomir Radev, Amanda Stent, Joel Tetreault, Aasish Pappu, Aikaterini Iliakopoulou, Agustin Chanfreau, Paloma de Juan, Jordi Vallmitjana, Alejandro Jaimes, Rahul Jha, Bob Mankoff
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