189 citations · 1.7k across the 110 of their papers we have counts for
30 papers · 1 filter
Learning Personalized End-to-End Goal-Oriented Dialog
Liangchen Luo, Wenhao Huang, Qi Zeng +2
Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved iss…
Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy
Pengcheng Yang, Fuli Luo, Shuangzhi Wu +3
Cross-lingual word embeddings aim to capture common linguistic regularities of different languages, which benefit various downstream tasks ranging from machine translation to trans…
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…
Evaluating Semantic Rationality of a Sentence: A Sememe-Word-Matching Neural Network based on HowNet
Shu Liu, Jingjing Xu, Xuancheng Ren +1
Automatic evaluation of semantic rationality is an important yet challenging task, and current automatic techniques cannot well identify whether a sentence is semantically rational…
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…
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation
Liangchen Luo, Jingjing Xu, Junyang Lin +2
Generating semantically coherent responses is still a major challenge in dialogue generation. Different from conventional text generation tasks, the mapping between inputs and resp…
simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions
Fenglin Liu, Xuancheng Ren, Yuanxin Liu +2
The encode-decoder framework has shown recent success in image captioning. Visual attention, which is good at detailedness, and semantic attention, which is good at comprehensivene…
A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation
Jingjing Xu, Xuancheng Ren, Yi Zhang +3
Narrative story generation is a challenging problem because it demands the generated sentences with tight semantic connections, which has not been well studied by most existing gen…
Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation
Junyang Lin, Xu Sun, Xuancheng Ren +2
Most of the Neural Machine Translation (NMT) models are based on the sequence-to-sequence (Seq2Seq) model with an encoder-decoder framework equipped with the attention mechanism. H…
Learning Sentiment Memories for Sentiment Modification without Parallel Data
Yi Zhang, Jingjing Xu, Pengcheng Yang +1
The task of sentiment modification requires reversing the sentiment of the input and preserving the sentiment-independent content. However, aligned sentences with the same content…
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,…
Sememe Prediction: Learning Semantic Knowledge from Unstructured Textual Wiki Descriptions
Wei Li, Xuancheng Ren, Damai Dai +3
Huge numbers of new words emerge every day, leading to a great need for representing them with semantic meaning that is understandable to NLP systems. Sememes are defined as the mi…
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…
Regularizing Output Distribution of Abstractive Chinese Social Media Text Summarization for Improved Semantic Consistency
Bingzhen Wei, Xuancheng Ren, Xu Sun +3
Abstractive text summarization is a highly difficult problem, and the sequence-to-sequence model has shown success in improving the performance on the task. However, the generated…
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…
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach
Jingjing Xu, Xu Sun, Qi Zeng +4
The goal of sentiment-to-sentiment "translation" is to change the underlying sentiment of a sentence while keeping its content. The main challenge is the lack of parallel data. To…
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…