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Found 36 papers, 17 papers with code
MTG: A Benchmarking Suite for Multilingual Text Generation
We introduce MTG, a new benchmark suite for training and evaluating multilingual text generation.
Duplex Sequence-to-Sequence Learning for Reversible Machine Translation
Sequence-to-sequence (seq2seq) problems such as machine translation are bidirectional, which naturally derive a pair of directional tasks and two directional learning signals.
A Gradient-based Kernel Approach for Efficient Network Architecture Search
Inspired by our new finding, we explore a simple yet effective network architecture search (NAS) approach that leverages gradient correlation and gradient values to find well-performing architectures.
Information-theoretic Vocabularization via Optimal Transport
In this paper, we find an exciting relation between an information-theoretic feature and the performance of NLP tasks such as machine translation with a given vocabulary.
Vocabulary Learning via Optimal Transport for Neural Machine Translation
The choice of token vocabulary affects the performance of machine translation.
Graph-based Multi-hop Reasoning for Long Text Generation
The reasoning module is responsible for searching skeleton paths from a knowledge graph to imitate the imagination process in the human writing for semantic transfer.
MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
In this work, we explore parallel multi-scale representation learning on sequence data, striving to capture both long-range and short-range language structures.
Ranked #8 on
Machine Translation
on WMT2014 English-French
Understanding and Improving Layer Normalization
Unlike them, we find that the derivatives of the mean and variance are more important than forward normalization by re-centering and re-scaling backward gradients.
Ranked #4 on
Machine Translation
on IWSLT2015 English-Vietnamese
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.
LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification
The generator learns to generate examples to attack the classifier while the classifier learns to defend these attacks.
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.
Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering
In this work, we propose to automatically extract evidence from heterogeneous knowledge sources, and answer questions based on the extracted evidence.
Ranked #5 on
Common Sense Reasoning
on CommonsenseQA
Reasoning Over Semantic-Level Graph for Fact Checking
We evaluate our system on FEVER, a benchmark dataset for fact checking, and find that rich structural information is helpful and both our graph-based mechanisms improve the accuracy.
Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model
In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph.
PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation
Chinese word segmentation (CWS) is a fundamental step of Chinese natural language processing.
Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model
In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph.
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.
Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation
Existing text generation methods tend to produce repeated and {''}boring{''} expressions.
Knowledge-Aware Conversational Semantic Parsing Over Web Tables
Results show that our knowledge-aware model outperforms the state-of-the-art approaches.
Evaluating Semantic Rationality of a Sentence: A Sememe-Word-Matching Neural Network based on HowNet
To evaluate the effectiveness of the proposed model, we build a large-scale rationality evaluation dataset.
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation
Different from conventional text generation tasks, the mapping between inputs and responses in conversations is more complicated, which highly demands the understanding of utterance-level semantic dependency, a relation between the whole meanings of inputs and outputs.
Ranked #2 on
Text Generation
on DailyDialog
Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations
This task requires the system to identify multiple styles of music based on its reviews on websites.
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.
A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation
Compared to the state-of-the-art models, our skeleton-based model can generate significantly more coherent text according to human evaluation and automatic evaluation.
Primal Meaning Recommendation via On-line Encyclopedia
We define the primal meaning of an expression to be a frequently used sense of that expression from which its other frequent senses can be deduced.
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach
We evaluate our approach on two review datasets, Yelp and Amazon.
Ranked #5 on
Unsupervised Text Style Transfer
on Yelp
DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text
Existing text generation methods tend to produce repeated and "boring" expressions.
A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text
To build a high quality dataset, we propose two tagging methods to solve the problem of data inconsistency, including a heuristic tagging method and a machine auxiliary tagging method.
Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method
Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications.
Deep Stacking Networks for Low-Resource Chinese Word Segmentation with Transfer Learning
In this paper, we propose a deep stacking framework to improve the performance on word segmentation tasks with insufficient data by integrating datasets from diverse domains.
Improving Social Media Text Summarization by Learning Sentence Weight Distribution
Recently, encoder-decoder models are widely used in social media text summarization.
Shallow Discourse Parsing with Maximum Entropy Model
The head-based representation of the PDTB is adopted in the arguments identifier, which turns the problem of indentifying the arguments of discourse connective into finding the head and end of the arguments.
Minimal Effort Back Propagation for Convolutional Neural Networks
As traditional neural network consumes a significant amount of computing resources during back propagation, \citet{Sun2017mePropSB} propose a simple yet effective technique to alleviate this problem.
Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization
In this work, our goal is to improve semantic relevance between source texts and summaries for Chinese social media summarization.
Transfer Deep Learning for Low-Resource Chinese Word Segmentation with a Novel Neural Network
First, we train a teacher model on high-resource corpora and then use the learned knowledge to initialize a student model.