276 citations · 2k across the 138 of their papers we have counts for
25 papers · 2 filters
Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model
Wenhan Xiong, Jingfei Du, William Yang Wang +1
Recent breakthroughs of pretrained language models have shown the effectiveness of self-supervised learning for a wide range of natural language processing (NLP) tasks. In addition…
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…
Cross-Lingual Vision-Language Navigation
An Yan, Xin Eric Wang, Jiangtao Feng +2
Commanding a robot to navigate with natural language instructions is a long-term goal for grounded language understanding and robotics. But the dominant language is English, accord…
A Benchmark Dataset for Learning to Intervene in Online Hate Speech
Jing Qian, Anna Bethke, Yinyin Liu +2
Countering online hate speech is a critical yet challenging task, but one which can be aided by the use of Natural Language Processing (NLP) techniques. Previous research has prima…
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization
Siyao Li, Deren Lei, Pengda Qin +1
Deep reinforcement learning (RL) has been a commonly-used strategy for the abstractive summarization task to address both the exposure bias and non-differentiable task issues. Howe…
Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification
Jiawei Wu, Wenhan Xiong, William Yang Wang
Many tasks in natural language processing can be viewed as multi-label classification problems. However, most of the existing models are trained with the standard cross-entropy los…
Neural Gaussian Copula for Variational Autoencoder
Prince Zizhuang Wang, William Yang Wang
Variational language models seek to estimate the posterior of latent variables with an approximated variational posterior. The model often assumes the variational posterior to be f…
Neural Correction Model for Open-Domain Named Entity Recognition
Mengdi Zhu, Zheye Deng, Wenhan Xiong +3
Named Entity Recognition (NER) plays an important role in a wide range of natural language processing tasks, such as relation extraction, question answering, etc. However, previous…
TabFact: A Large-scale Dataset for Table-based Fact Verification
Wenhu Chen, Hongmin Wang, Jianshu Chen +5
The problem of verifying whether a textual hypothesis holds based on the given evidence, also known as fact verification, plays an important role in the study of natural language u…
Text Modeling with Syntax-Aware Variational Autoencoders
Yijun Xiao, William Yang Wang
Syntactic information contains structures and rules about how text sentences are arranged. Incorporating syntax into text modeling methods can potentially benefit both representati…
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…
Multi-Task Self-Supervised Learning for Disfluency Detection
Shaolei Wang, Wanxiang Che, Qi Liu +3
Most existing approaches to disfluency detection heavily rely on human-annotated data, which is expensive to obtain in practice. To tackle the training data bottleneck, we investig…
What Should I Ask? Using Conversationally Informative Rewards for Goal-Oriented Visual Dialog
Pushkar Shukla, Carlos Elmadjian, Richika Sharan +3
The ability to engage in goal-oriented conversations has allowed humans to gain knowledge, reduce uncertainty, and perform tasks more efficiently. Artificial agents, however, are s…
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 Dialogue Learning
Jiawei Wu, Xin Wang, William Yang Wang
The sequential order of utterances is often meaningful in coherent dialogues, and the order changes of utterances could lead to low-quality and incoherent conversations. We conside…
Mitigating Gender Bias in Natural Language Processing: Literature Review
Tony Sun, Andrew Gaut, Shirlyn Tang +7
As Natural Language Processing (NLP) and Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases an…
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.…
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention
Wenhu Chen, Jianshu Chen, Pengda Qin +2
Semantically controlled neural response generation on limited-domain has achieved great performance. However, moving towards multi-domain large-scale scenarios are shown to be diff…
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…
Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling
Prince Zizhuang Wang, William Yang Wang
Recurrent Variational Autoencoder has been widely used for language modeling and text generation tasks. These models often face a difficult optimization problem, also known as the…
Learning to Decipher Hate Symbols
Jing Qian, Mai ElSherief, Elizabeth Belding +1
Existing computational models to understand hate speech typically frame the problem as a simple classification task, bypassing the understanding of hate symbols (e.g., 14 words, ki…
Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation
Jiawei Wu, Xin Wang, William Yang Wang
The overreliance on large parallel corpora significantly limits the applicability of machine translation systems to the majority of language pairs. Back-translation has been domina…
Few-Shot NLG with Pre-Trained Language Model
Zhiyu Chen, Harini Eavani, Wenhu Chen +2
Neural-based end-to-end approaches to natural language generation (NLG) from structured data or knowledge are data-hungry, making their adoption for real-world applications difficu…
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…