189 citations · 1.7k across the 110 of their papers we have counts for
22 papers · 1 filter
Visual Agreement Regularized Training for Multi-Modal Machine Translation
Pengcheng Yang, Boxing Chen, Pei Zhang +1
Multi-modal machine translation aims at translating the source sentence into a different language in the presence of the paired image. Previous work suggests that additional visual…
Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection
Guangxiang Zhao, Junyang Lin, Zhiyuan Zhang +3
Self-attention based Transformer has demonstrated the state-of-the-art performances in a number of natural language processing tasks. Self-attention is able to model long-term depe…
MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
Guangxiang Zhao, Xu Sun, Jingjing Xu +2
In sequence to sequence learning, the self-attention mechanism proves to be highly effective, and achieves significant improvements in many tasks. However, the self-attention mecha…
Understanding and Improving Layer Normalization
Jingjing Xu, Xu Sun, Zhiyuan Zhang +2
Layer normalization (LayerNorm) is a technique to normalize the distributions of intermediate layers. It enables smoother gradients, faster training, and better generalization accu…
HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network
Deli Chen, Xiaoqian Liu, Yankai Lin +4
Graph Neural Networks (GNNs) are efficient approaches to process graph-structured data. Modelling long-distance node relations is essential for GNN training and applications. Howev…
An Adaptive and Momental Bound Method for Stochastic Learning
Jianbang Ding, Xuancheng Ren, Ruixuan Luo +1
Training deep neural networks requires intricate initialization and careful selection of learning rates. The emergence of stochastic gradient optimization methods that use adaptive…
Pun-GAN: Generative Adversarial Network for Pun Generation
Fuli Luo, Shunyao Li, Pengcheng Yang +4
In this paper, we focus on the task of generating a pun sentence given a pair of word senses. A major challenge for pun generation is the lack of large-scale pun corpus to guide th…
Aligning Cross-Lingual Entities with Multi-Aspect Information
Hsiu-Wei Yang, Yanyan Zou, Peng Shi +3
Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages. The task of cross-lingual entity alignment is to match entities in a sourc…
Incorporating Fine-grained Events in Stock Movement Prediction
Deli Chen, Yanyan Zou, Keiko Harimoto +3
Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the…
Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction
Deli Chen, Shuming ma, Keiko Harimoto +3
Incorporating related text information has proven successful in stock market prediction. However, it is a huge challenge to utilize texts in the enormous forex (foreign currency ex…
Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting
Yi Zhang, Tao Ge, Furu Wei +2
We study sequence-to-sequence (seq2seq) pre-training with data augmentation for sentence rewriting. Instead of training a seq2seq model with gold training data and augmented data s…
Recursive Graphical Neural Networks for Text Classification
Wei Li, Shuheng Li, Shuming Ma +3
The complicated syntax structure of natural language is hard to be explicitly modeled by sequence-based models. Graph is a natural structure to describe the complicated relation be…
Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
Deli Chen, Yankai Lin, Wei Li +3
Graph Neural Networks (GNNs) have achieved promising performance on a wide range of graph-based tasks. Despite their success, one severe limitation of GNNs is the over-smoothing is…
Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation
Shuming Ma, Pengcheng Yang, Tianyu Liu +3
Table-to-text generation aims to translate the structured data into the unstructured text. Most existing methods adopt the encoder-decoder framework to learn the transformation, wh…
Imitation Learning for Non-Autoregressive Neural Machine Translation
Bingzhen Wei, Mingxuan Wang, Hao Zhou +3
Non-autoregressive translation models (NAT) have achieved impressive inference speedup. A potential issue of the existing NAT algorithms, however, is that the decoding is conducted…
A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer
Chen Wu, Xuancheng Ren, Fuli Luo +1
Unsupervised text style transfer aims to alter text styles while preserving the content, without aligned data for supervision. Existing seq2seq methods face three challenges: 1) th…
Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model
Wei Li, Jingjing Xu, Yancheng He +3
Automatic article commenting is helpful in encouraging user engagement and interaction on online news platforms. However, the news documents are usually too long for traditional en…
PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation
Ruixuan Luo, Jingjing Xu, Yi Zhang +3
Chinese word segmentation (CWS) is a fundamental step of Chinese natural language processing. In this paper, we build a new toolkit, named PKUSEG, for multi-domain word segmentatio…
A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer
Fuli Luo, Peng Li, Jie Zhou +4
Unsupervised text style transfer aims to transfer the underlying style of text but keep its main content unchanged without parallel data. Most existing methods typically follow two…
Memorized Sparse Backpropagation
Zhiyuan Zhang, Pengcheng Yang, Xuancheng Ren +2
Neural network learning is usually time-consuming since backpropagation needs to compute full gradients and backpropagate them across multiple layers. Despite its success of existi…
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations
Fenglin Liu, Yuanxin Liu, Xuancheng Ren +2
In vision-and-language grounding problems, fine-grained representations of the image are considered to be of paramount importance. Most of the current systems incorporate visual fe…
Adaptive Gradient Methods with Dynamic Bound of Learning Rate
Liangchen Luo, Yuanhao Xiong, Yan Liu +1
Adaptive optimization methods such as AdaGrad, RMSprop and Adam have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates. Though p…