108 citations · 413 across the 31 of their papers we have counts for
31 papers
XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders
Shuming Ma, Jian Yang, Haoyang Huang +10
Multilingual machine translation enables a single model to translate between different languages. Most existing multilingual machine translation systems adopt a randomly initialize…
Multimodal Matching Transformer for Live Commenting
Chaoqun Duan, Lei Cui, Shuming Ma +3
Automatic live commenting aims to provide real-time comments on videos for viewers. It encourages users engagement on online video sites, and is also a good benchmark for video-to-…
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…
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…
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…
Unsupervised Machine Commenting with Neural Variational Topic Model
Shuming Ma, Lei Cui, Furu Wei +1
Article comments can provide supplementary opinions and facts for readers, thereby increase the attraction and engagement of articles. Therefore, automatically commenting is helpfu…
A Deep Reinforced Sequence-to-Set Model for Multi-Label Text Classification
Pengcheng Yang, Shuming Ma, Yi Zhang +3
Multi-label text classification (MLTC) aims to assign multiple labels to each sample in the dataset. The labels usually have internal correlations. However, traditional methods ten…
LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts
Shuming Ma, Lei Cui, Damai Dai +2
We introduce the task of automatic live commenting. Live commenting, which is also called `video barrage', is an emerging feature on online video sites that allows real-time commen…
Identifying High-Quality Chinese News Comments Based on Multi-Target Text Matching Model
Deli Chen, Shuming Ma, Pengcheng Yang +1
With the development of information technology, there is an explosive growth in the number of online comment concerning news, blogs and so on. The massive comments are overloaded,…
Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification
Junyang Lin, Qi Su, Pengcheng Yang +2
We propose a novel model for multi-label text classification, which is based on sequence-to-sequence learning. The model generates higher-level semantic unit representations with m…
SGM: Sequence Generation Model for Multi-label Classification
Pengcheng Yang, Xu Sun, Wei Li +3
Multi-label classification is an important yet challenging task in natural language processing. It is more complex than single-label classification in that the labels tend to be co…
Deconvolution-Based Global Decoding for Neural Machine Translation
Junyang Lin, Xu Sun, Xuancheng Ren +3
A great proportion of sequence-to-sequence (Seq2Seq) models for Neural Machine Translation (NMT) adopt Recurrent Neural Network (RNN) to generate translation word by word following…
Global Encoding for Abstractive Summarization
Junyang Lin, Xu Sun, Shuming Ma +1
In neural abstractive summarization, the conventional sequence-to-sequence (seq2seq) model often suffers from repetition and semantic irrelevance. To tackle the problem, we propose…
A Hierarchical End-to-End Model for Jointly Improving Text Summarization and Sentiment Classification
Shuming Ma, Xu Sun, Junyang Lin +1
Text summarization and sentiment classification both aim to capture the main ideas of the text but at different levels. Text summarization is to describe the text within a few sent…
Bag-of-Words as Target for Neural Machine Translation
Shuming Ma, Xu Sun, Yizhong Wang +1
A sentence can be translated into more than one correct sentences. However, most of the existing neural machine translation models only use one of the correct translations as the t…
Autoencoder as Assistant Supervisor: Improving Text Representation for Chinese Social Media Text Summarization
Shuming Ma, Xu Sun, Junyang Lin +1
Most of the current abstractive text summarization models are based on the sequence-to-sequence model (Seq2Seq). The source content of social media is long and noisy, so it is diff…
Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network
Pengcheng Yang, Xu Sun, Wei Li +1
As more and more academic papers are being submitted to conferences and journals, evaluating all these papers by professionals is time-consuming and can cause inequality due to the…
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
Shuming Ma, Xu Sun, Wei Li +3
Most recent approaches use the sequence-to-sequence model for paraphrase generation. The existing sequence-to-sequence model tends to memorize the words and the patterns in the tra…
Decoding-History-Based Adaptive Control of Attention for Neural Machine Translation
Junyang Lin, Shuming Ma, Qi Su +1
Attention-based sequence-to-sequence model has proved successful in Neural Machine Translation (NMT). However, the attention without consideration of decoding history, which includ…
Complex Structure Leads to Overfitting: A Structure Regularization Decoding Method for Natural Language Processing
Xu Sun, Weiwei Sun, Shuming Ma +4
Recent systems on structured prediction focus on increasing the level of structural dependencies within the model. However, our study suggests that complex structures entail high o…
Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?
Yi Zhang, Xu Sun, Shuming Ma +2
Existing neural models usually predict the tag of the current token independent of the neighboring tags. The popular LSTM-CRF model considers the tag dependencies between every two…
Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method
Xu Sun, Xuancheng Ren, Shuming Ma +5
We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is co…
Scalable Peaceman-Rachford Splitting Method with Proximal Terms
Sen Na, Mingyuan Ma, Mladen Kolar
Along with developing of Peaceman-Rachford Splittling Method (PRSM), many batch algorithms based on it have been studied very deeply. But almost no algorithm focused on the perform…
Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks
Xu Sun, Bingzhen Wei, Xuancheng Ren +1
We propose a method, called Label Embedding Network, which can learn label representation (label embedding) during the training process of deep networks. With the proposed method,…