Search Results for author: Dragomir Radev

Found 83 papers, 40 papers with code

Improving Cross-lingual Text Classification with Zero-shot Instance-Weighting

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.

Classification Text Classification +1

Leveraging Locality in Abstractive Text Summarization

no code implementations25 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.

Abstractive Text Summarization Text Generation

Twist Decoding: Diverse Generators Guide Each Other

1 code implementation19 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.

Machine Translation Text Generation

Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions

1 code implementation15 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.

graph construction Graph Learning +1

EHRKit: A Python Natural Language Processing Toolkit for Electronic Health Record Texts

1 code implementation13 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.

Information Retrieval Machine Translation +2

BRIO: Bringing Order to Abstractive Summarization

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.

Abstractive Text Summarization

CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning

no code implementations16 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.

Abstractive Dialogue Summarization Meeting Summarization +1

Surfer100: Generating Surveys From Web Resources, Wikipedia-style

no code implementations13 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.

Language Modelling

Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs

2 code implementations4 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.

graph construction Translation

Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries

no code implementations19 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.

Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains

no code implementations17 Sep 2021 Irene Li, Vanessa Yan, Dragomir Radev

Our novel model consists of a variational graph autoencoder (VGAE) and a domain discriminator.

Link Prediction

An Exploratory Study on Long Dialogue Summarization: What Works and What's Next

1 code implementation10 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.

SummerTime: Text Summarization Toolkit for Non-experts

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.

Document Summarization Multi-Document Summarization

ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining

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.

Abstractive Text Summarization Argument Mining +2

BookSum: A Collection of Datasets for Long-form Narrative Summarization

1 code implementation18 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.

Abstractive Text Summarization

Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders

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).

QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization

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.

Meeting Summarization

FeTaQA: Free-form Table Question Answering

1 code implementation1 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.

Question Answering Semantic Parsing +1

Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start

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.

Coreference Resolution Natural Language Inference +1

GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing

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.

Language Modelling Masked Language Modeling +2

SummEval: Re-evaluating Summarization Evaluation

5 code implementations24 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.

Text Summarization

CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization

no code implementations17 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.

Abstractive Text Summarization Information Retrieval +2

ESPRIT: Explaining Solutions to Physical Reasoning Tasks

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.

R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning

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.


A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation

1 code implementation30 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.

Few-Shot Learning

The CL-SciSumm Shared Task 2018: Results and Key Insights

1 code implementation2 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.

Document Summarization Information Retrieval +1

Creating A Neural Pedagogical Agent by Jointly Learning to Review and Assess

2 code implementations26 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.

Machine Translation TAG

SParC: Cross-Domain Semantic Parsing in Context

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.

Semantic Parsing Text-To-Sql

Syntax-aware Neural Semantic Role Labeling with Supertags

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.

Semantic Role Labeling TAG

SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task

2 code implementations11 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.

Semantic Parsing Text-To-Sql

Improving Text-to-SQL Evaluation Methodology

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.

SQL Parsing Text-To-Sql

TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation

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.

Slot Filling Text-To-Sql

Robust Multilingual Part-of-Speech Tagging via Adversarial Training

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.

Chunking Dependency Parsing +3

Addressee and Response Selection in Multi-Party Conversations with Speaker Interaction RNNs

1 code implementation12 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.

Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents

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.

Classification General Classification +3

Sentence Similarity based on Dependency Tree Kernels for Multi-document Summarization

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.

Document Summarization Multi-Document Summarization +2

Extractive Summarization under Strict Length Constraints

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).

Extractive Summarization

Classifying Syntactic Regularities for Hundreds of Languages

no code implementations25 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).

General Classification

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