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Found 29 papers, 13 papers with code
Rethinking Denoised Auto-Encoding in Language Pre-Training
Pre-trained self-supervised models such as BERT have achieved striking success in learning sequence representations, especially for natural language processing.
Group-based Interleaved Pipeline Parallelism for Large-scale DNN Training
The recent trend of using large-scale deep neural networks (DNN) to boost performance has propelled the development of the parallel pipelining technique for efficient DNN training, which has resulted in the development of several prominent pipelines such as GPipe, PipeDream, and PipeDream-2BW.
Context-Interactive Pre-Training for Document Machine Translation
Document machine translation aims to translate the source sentence into the target language in the presence of additional contextual information.
CAPT: Contrastive Pre-Training for Learning Denoised Sequence Representations
Pre-trained self-supervised models such as BERT have achieved striking success in learning sequence representations, especially for natural language processing.
Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions
Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training.
Visual Agreement Regularized Training for Multi-Modal Machine Translation
Further analysis demonstrates that the proposed regularized training can effectively improve the agreement of attention on the image, leading to better use of visual information.
Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification
The task of unsupervised sentiment modification aims to reverse the sentiment polarity of the input text while preserving its semantic content without any parallel data.
Asking Clarification Questions in Knowledge-Based Question Answering
We provide representative baselines for these tasks and further introduce a coarse-to-fine model for clarification question generation.
Pun-GAN: Generative Adversarial Network for Pun Generation
It consists of a generator to produce pun sentences, and a discriminator to distinguish between the generated pun sentences and the real sentences with specific word senses.
Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation
We propose a novel model to separate the generation into two stages: key fact prediction and surface realization.
Automatic Generation of Personalized Comment Based on User Profile
Comments on social media are very diverse, in terms of content, style and vocabulary, which make generating comments much more challenging than other existing natural language generation~(NLG) tasks.
A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification
In this way, we can reduce the dependence of the model on the label order, as well as capture high-order correlations between labels.
Towards Comprehensive Description Generation from Factual Attribute-value Tables
To relieve these problems, we first propose force attention (FA) method to encourage the generator to pay more attention to the uncovered attributes to avoid potential key attributes missing.
Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information
Automatic commenting of online articles can provide additional opinions and facts to the reader, which improves user experience and engagement on social media platforms.
Enhancing Topic-to-Essay Generation with External Commonsense Knowledge
Experiments show that with external commonsense knowledge and adversarial training, the generated essays are more novel, diverse, and topic-consistent than existing methods in terms of both automatic and human evaluation.
MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction
The task of unsupervised bilingual lexicon induction (UBLI) aims to induce word translations from monolingual corpora in two languages.
Learning to Control the Fine-grained Sentiment for Story Ending Generation
Therefore, we propose a generic and novel framework which consists of a sentiment analyzer and a sentimental generator, respectively addressing the two challenges.
Towards Fine-grained Text Sentiment Transfer
In this paper, we focus on the task of fine-grained text sentiment transfer (FGST).
Memorized Sparse Backpropagation
Neural network learning is usually time-consuming since backpropagation needs to compute full gradients and backpropagate them across multiple layers.
A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer
Therefore, in this paper, we propose a dual reinforcement learning framework to directly transfer the style of the text via a one-step mapping model, without any separation of content and style.
Ranked #1 on
Unsupervised Text Style Transfer
on GYAFC
Knowledgeable Storyteller: A Commonsense-Driven Generative Model for Visual Storytelling
The visual storytelling (VST) task aims at generating a reasonable and coherent paragraph-level story with the image stream as input.
Ranked #21 on
Visual Storytelling
on VIST
Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy
In order to avoid such sophisticated alternate optimization, we propose to learn unsupervised word mapping by directly maximizing the mean discrepancy between the distribution of transferred embedding and target embedding.
A Deep Reinforced Sequence-to-Set Model for Multi-Label Text Classification
However, the Seq2Seq model is not suitable for the MLTC task in essence.
Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification
We propose a novel model for multi-label text classification, which is based on sequence-to-sequence learning.
Learning Sentiment Memories for Sentiment Modification without Parallel Data
The task of sentiment modification requires reversing the sentiment of the input and preserving the sentiment-independent content.
Identifying High-Quality Chinese News Comments Based on Multi-Target Text Matching Model
In this work, we introduce a novel task: high-quality comment identification (HQCI), which aims to automatically assess the quality of online comments.
SGM: Sequence Generation Model for Multi-label Classification
Further analysis of experimental results demonstrates that the proposed methods not only capture the correlations between labels, but also select the most informative words automatically when predicting different labels.
Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network
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 personal factors of the reviewers.
