265 citations · 1.9k across the 79 of their papers we have counts for
20 papers · 1 filter
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering
Wenhan Xiong, Mo Yu, Xiaoxiao Guo +4
A key challenge of multi-hop question answering (QA) in the open-domain setting is to accurately retrieve the supporting passages from a large corpus. Existing work on open-domain…
A Game Theoretic Approach to Class-wise Selective Rationalization
Shiyu Chang, Yang Zhang, Mo Yu +1
Selection of input features such as relevant pieces of text has become a common technique of highlighting how complex neural predictors operate. The selection can be optimized post…
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control
Mo Yu, Shiyu Chang, Yang Zhang +1
Selective rationalization has become a common mechanism to ensure that predictive models reveal how they use any available features. The selection may be soft or hard, and identifi…
An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack
Yang Zhang, Shiyu Chang, Mo Yu +1
There are two major paradigms of white-box adversarial attacks that attempt to impose input perturbations. The first paradigm, called the fix-perturbation attack, crafts adversaria…
Out-of-Domain Detection for Low-Resource Text Classification Tasks
Ming Tan, Yang Yu, Haoyu Wang +4
Out-of-domain (OOD) detection for low-resource text classification is a realistic but understudied task. The goal is to detect the OOD cases with limited in-domain (ID) training da…
Meta Reasoning over Knowledge Graphs
Hong Wang, Wenhan Xiong, Mo Yu +3
The ability to reason over learned knowledge is an innate ability for humans and humans can easily master new reasoning rules with only a few demonstrations. While most existing st…
AutoGAN: Neural Architecture Search for Generative Adversarial Networks
Xinyu Gong, Shiyu Chang, Yifan Jiang +1
Neural architecture search (NAS) has witnessed prevailing success in image classification and (very recently) segmentation tasks. In this paper, we present the first preliminary st…
Few-shot Text Classification with Distributional Signatures
Yujia Bao, Menghua Wu, Shiyu Chang +1
In this paper, we explore meta-learning for few-shot text classification. Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable a…
TWEETQA: A Social Media Focused Question Answering Dataset
Wenhan Xiong, Jiawei Wu, Hong Wang +5
With social media becoming increasingly pop-ular on which lots of news and real-time eventsare reported, developing automated questionanswering systems is critical to the effective…
Self-Supervised Learning for Contextualized Extractive Summarization
Hong Wang, Xin Wang, Wenhan Xiong +4
Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss, which does not explicitly capture the global context at the document level.…
Coupled Variational Recurrent Collaborative Filtering
Qingquan Song, Shiyu Chang, Xia Hu
We focus on the problem of streaming recommender system and explore novel collaborative filtering algorithms to handle the data dynamicity and complexity in a streaming manner. Alt…
Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets
Guanhua Zhang, Bing Bai, Jian Liang +5
Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process. However,…
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
Kaizhi Qian, Yang Zhang, Shiyu Chang +2
Non-parallel many-to-many voice conversion, as well as zero-shot voice conversion, remain under-explored areas. Deep style transfer algorithms, such as generative adversarial netwo…
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers
Guang-He Lee, Yang Yuan, Shiyu Chang +1
Strong theoretical guarantees of robustness can be given for ensembles of classifiers generated by input randomization. Specifically, an bounded adversary cannot alter the…
Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader
Wenhan Xiong, Mo Yu, Shiyu Chang +2
We propose a new end-to-end question answering model, which learns to aggregate answer evidence from an incomplete knowledge base (KB) and a set of retrieved text snippets. Under t…
Additive Adversarial Learning for Unbiased Authentication
Jian Liang, Yuren Cao, Chenbin Zhang +3
Authentication is a task aiming to confirm the truth between data instances and personal identities. Typical authentication applications include face recognition, person re-identif…
Sentence Embedding Alignment for Lifelong Relation Extraction
Hong Wang, Wenhan Xiong, Mo Yu +3
Conventional approaches to relation extraction usually require a fixed set of pre-defined relations. Such requirement is hard to meet in many real applications, especially when new…
Hybrid Reinforcement Learning with Expert State Sequences
Xiaoxiao Guo, Shiyu Chang, Mo Yu +2
Existing imitation learning approaches often require that the complete demonstration data, including sequences of actions and states, are available. In this paper, we consider a mo…
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing
Wenhan Xiong, Jiawei Wu, Deren Lei +4
Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance. Such…
Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
Haoyu Wang, Ming Tan, Mo Yu +5
Most approaches to extraction multiple relations from a paragraph require multiple passes over the paragraph. In practice, multiple passes are computationally expensive and this ma…