833 citations · 2k across the 50 of their papers we have counts for
51 papers
Hybrid Forecasting of Geopolitical Events
Daniel M. Benjamin, Fred Morstatter, Ali E. Abbas +26
Sound decision-making relies on accurate prediction for tangible outcomes ranging from military conflict to disease outbreaks. To improve crowdsourced forecasting accuracy, we deve…
Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection
Jun Yan, Vikas Yadav, Shiyang Li +6
Instruction-tuned Large Language Models (LLMs) have become a ubiquitous platform for open-ended applications due to their ability to modulate responses based on human instructions.…
Faith and Fate: Limits of Transformers on Compositionality
Nouha Dziri, Ximing Lu, Melanie Sclar +13
Transformer large language models (LLMs) have sparked admiration for their exceptional performance on tasks that demand intricate multi-step reasoning. Yet, these models simultaneo…
AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding
Jun Yan, Nasser Zalmout, Yan Liang +3
Automatic extraction of product attribute values is an important enabling technology in e-Commerce platforms. This task is usually modeled using sequence labeling architectures, wi…
X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering
Meryem M'hamdi, Doo Soon Kim, Franck Dernoncourt +3
Multilingual models, such as M-BERT and XLM-R, have gained increasing popularity, due to their zero-shot cross-lingual transfer learning capabilities. However, their generalization…
On the Influence of Masking Policies in Intermediate Pre-training
Qinyuan Ye, Belinda Z. Li, Sinong Wang +5
Current NLP models are predominantly trained through a two-stage "pre-train then fine-tune" pipeline. Prior work has shown that inserting an intermediate pre-training stage, using…
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP
Qinyuan Ye, Bill Yuchen Lin, Xiang Ren
Humans can learn a new language task efficiently with only few examples, by leveraging their knowledge obtained when learning prior tasks. In this paper, we explore whether and how…
SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning
Aaron Chan, Jiashu Xu, Boyuan Long +3
Augmenting pre-trained language models with knowledge graphs (KGs) has achieved success on various commonsense reasoning tasks. However, for a given task instance, the KG, or certa…
Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation
Yuning Mao, Wenchang Ma, Deren Lei +2
Prior studies on text-to-text generation typically assume that the model could figure out what to attend to in the input and what to include in the output via seq2seq learning, wit…
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources
Ninareh Mehrabi, Pei Zhou, Fred Morstatter +3
Warning: this paper contains content that may be offensive or upsetting. Numerous natural language processing models have tried injecting commonsense by using the ConceptNet knowle…
Social Media Polarization and Echo Chambers in the Context of COVID-19: Case Study
Julie Jiang, Xiang Ren, Emilio Ferrara
Background: Social media chatter in 2020 has been largely dominated by the COVID-19 pandemic. Existing research shows that COVID-19 discourse is highly politicized, with political…
Structured Distributed Compressive Channel Estimation over Doubly Selective Channels
Qibo Qin, Lin Gui, Bo Gong +2
For an orthogonal frequency-division multiplexing (OFDM) system over a doubly selective (DS) channel, a large number of pilot subcarriers are needed to estimate the numerous channe…
Block Distributed Compressive Sensing Based Doubly Selective Channel Estimation and Pilot Design for Large-Scale MIMO Systems
Bo Gong, Lin Gui, Qibo Qin +2
The doubly selective (DS) channel estimation in the large-scale multiple-input multiple-output (MIMO) systems is a challenging problem due to the large number of the channel coeffi…
Position-Based Interference Elimination for High Mobility OFDM Channel Estimation in Multi-cell Systems
Xiang Ren, Wen Chen, Bo Gong +2
Orthogonal frequency-division multiplexing (OFD-M) and multi-cell architecture are widely adopted in current high speed train (HST) systems for providing high data rate wireless co…
Position Based Compressed Channel Estimation and Pilot Design for High Mobility OFDM Systems
Xiang Ren, Wen Chen, Meixia Tao
With the development of high speed trains (HST) in many countries, providing broadband wireless services in HSTs is becoming crucial. Orthogonal frequency-division multiplexing (OF…
Compressed Channel Estimation with Position-Based ICI Elimination for High-Mobility SIMO-OFDM Systems
Xiang Ren, Meixia Tao, Wen Chen
Orthogonal frequency-division multiplexing (OFDM) is widely adopted for providing reliable and high data rate communication in high-speed train systems. However, with the increasin…
Mining News Events from Comparable News Corpora: A Multi-Attribute Proximity Network Modeling Approach
Hyungsul Kim, Ahmed El-Kishky, Xiang Ren +1
We present ProxiModel, a novel event mining framework for extracting high-quality structured event knowledge from large, redundant, and noisy news data sources. The proposed model…
Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models
Xisen Jin, Zhongyu Wei, Junyi Du +2
The impressive performance of neural networks on natural language processing tasks attributes to their ability to model complicated word and phrase compositions. To explain how the…
HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion
Jiaming Shen, Zeqiu Wu, Dongming Lei +5
Taxonomies are of great value to many knowledge-rich applications. As the manual taxonomy curation costs enormous human effects, automatic taxonomy construction is in great demand.…
SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble
Jiaming Shen, Zeqiu Wu, Dongming Lei +3
Corpus-based set expansion (i.e., finding the "complete" set of entities belonging to the same semantic class, based on a given corpus and a tiny set of seeds) is a critical task i…
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
Bill Yuchen Lin, Xinyue Chen, Jamin Chen +1
Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life. In this paper, we propose a textual inferenc…
Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes
Aida Mostafazadeh Davani, Leigh Yeh, Mohammad Atari +8
Official reports of hate crimes in the US are under-reported relative to the actual number of such incidents. Further, despite statistical approximations, there are no official rep…
Learning Dynamic Context Augmentation for Global Entity Linking
Xiyuan Yang, Xiaotao Gu, Sheng Lin +6
Despite of the recent success of collective entity linking (EL) methods, these "global" inference methods may yield sub-optimal results when the "all-mention coherence" assumption…
Collaborative Policy Learning for Open Knowledge Graph Reasoning
Cong Fu, Tong Chen, Meng Qu +2
In recent years, there has been a surge of interests in interpretable graph reasoning methods. However, these models often suffer from limited performance when working on sparse an…