193 citations · 1.5k across the 86 of their papers we have counts for
22 papers · 1 filter
Multi-channel Reverse Dictionary Model
Lei Zhang, Fanchao Qi, Zhiyuan Liu +3
A reverse dictionary takes the description of a target word as input and outputs the target word together with other words that match the description. Existing reverse dictionary m…
Deep Learning-Based Feature-Aware Data Modeling for Complex Physics Simulations
Qun Liu, Subhashis Hazarika, John M. Patchett +2
Data modeling and reduction for in situ is important. Feature-driven methods for in situ data analysis and reduction are a priority for future exascale machines as there are curren…
Learning to Predict Explainable Plots for Neural Story Generation
Gang Chen, Yang Liu, Huanbo Luan +3
Story generation is an important natural language processing task that aims to generate coherent stories automatically. While the use of neural networks has proven effective in imp…
Integrating Graph Contextualized Knowledge into Pre-trained Language Models
Bin He, Di Zhou, Jinghui Xiao +4
Complex node interactions are common in knowledge graphs, and these interactions also contain rich knowledge information. However, traditional methods usually treat a triple as a t…
Deep-seismic-prior-based reconstruction of seismic data using convolutional neural networks
Qun Liu, Lihua Fu, Meng Zhang
Reconstruction of seismic data with missing traces is a long-standing issue in seismic data processing. In recent years, rank reduction operations are being commonly utilized to ov…
DeepSat V2: Feature Augmented Convolutional Neural Nets for Satellite Image Classification
Qun Liu, Saikat Basu, Sangram Ganguly +4
Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent i…
Zero-Shot Paraphrase Generation with Multilingual Language Models
Yinpeng Guo, Yi Liao, Xin Jiang +3
Leveraging multilingual parallel texts to automatically generate paraphrases has drawn much attention as size of high-quality paraphrase corpus is limited. Round-trip translation,…
Pretrained Language Models for Document-Level Neural Machine Translation
Liangyou Li, Xin Jiang, Qun Liu
Previous work on document-level NMT usually focuses on limited contexts because of degraded performance on larger contexts. In this paper, we investigate on using large contexts wi…
A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation
Yun Chen, Liangyou Li, Xin Jiang +2
Despite the success of neural machine translation (NMT), simultaneous neural machine translation (SNMT), the task of translating in real time before a full sentence has been observ…
Word-level Textual Adversarial Attacking as Combinatorial Optimization
Yuan Zang, Fanchao Qi, Chenghao Yang +4
Adversarial attacks are carried out to reveal the vulnerability of deep neural networks. Textual adversarial attacking is challenging because text is discrete and a small perturbat…
Improving Sequence Modeling Ability of Recurrent Neural Networks via Sememes
Yujia Qin, Fanchao Qi, Sicong Ouyang +5
Sememes, the minimum semantic units of human languages, have been successfully utilized in various natural language processing applications. However, most existing studies exploit…
TinyBERT: Distilling BERT for Natural Language Understanding
Xiaoqi Jiao, Yichun Yin, Lifeng Shang +5
Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, pre-trained language models are usually c…
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
Junqiu Wei, Xiaozhe Ren, Xiaoguang Li +7
The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized informati…
PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters
Qun Liu, Edward Collier, Supratik Mukhopadhyay
Due to the sparsity of features, noise has proven to be a great inhibitor in the classification of handwritten characters. To combat this, most techniques perform denoising of the…
Dialog State Tracking with Reinforced Data Augmentation
Yichun Yin, Lifeng Shang, Xin Jiang +2
Neural dialog state trackers are generally limited due to the lack of quantity and diversity of annotated training data. In this paper, we address this difficulty by proposing a re…
Modeling Semantic Compositionality with Sememe Knowledge
Fanchao Qi, Junjie Huang, Chenghao Yang +4
Semantic compositionality (SC) refers to the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. Most related works focus…
Decomposable Neural Paraphrase Generation
Zichao Li, Xin Jiang, Lifeng Shang +1
Paraphrasing exists at different granularity levels, such as lexical level, phrasal level and sentential level. This paper presents Decomposable Neural Paraphrase Generator (DNPG),…
Bridging the Gap between Training and Inference for Neural Machine Translation
Wen Zhang, Yang Feng, Fandong Meng +2
Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words. At training time, it predicts with the…
ERNIE: Enhanced Language Representation with Informative Entities
Zhengyan Zhang, Xu Han, Zhiyuan Liu +3
Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently imp…
Why do you take that route?
Alimire Nabijiang, Supratik Mukhopadhyay, Yimin Zhu +3
The purpose of this paper is to determine whether a particular context factor among the variables that a researcher is interested in causally affects the route choice behavior of d…
Improving Domain Adaptation Translation with Domain Invariant and Specific Information
Shuhao Gu, Yang Feng, Qun Liu
In domain adaptation for neural machine translation, translation performance can benefit from separating features into domain-specific features and common features. In this paper,…
Improving Route Choice Models by Incorporating Contextual Factors via Knowledge Distillation
Qun Liu, Supratik Mukhopadhyay, Yimin Zhu +3
Route Choice Models predict the route choices of travelers traversing an urban area. Most of the route choice models link route characteristics of alternative routes to those chose…