activity
20172022
most citedAutomatically designing CNN architectures using genetic algorithm for image classification

883 citations · 1.1k across the 30 of their papers we have counts for

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Showing cs.CLShow all

16 papers · 1 filter

cs.CL2022

Draft, Command, and Edit: Controllable Text Editing in E-Commerce

Kexin Yang, Dayiheng Liu, Wenqiang Lei +3

Product description generation is a challenging and under-explored task. Most such work takes a set of product attributes as inputs then generates a description from scratch in a s…

cs.CL2021★ 22 cited

Poolingformer: Long Document Modeling with Pooling Attention

Hang Zhang, Yeyun Gong, Yelong Shen +4

In this paper, we introduce a two-level attention schema, Poolingformer, for long document modeling. Its first level uses a smaller sliding window pattern to aggregate information…

cs.CL2020★ 9 cited

GLGE: A New General Language Generation Evaluation Benchmark

Dayiheng Liu, Yu Yan, Yeyun Gong +15

Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP). These benchmarks mostly focus…

cs.CL2020★ 1 cited

Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space

Dayiheng Liu, Yeyun Gong, Jie Fu +5

In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC),…

cs.CL2020★ 2 cited

AnchiBERT: A Pre-Trained Model for Ancient ChineseLanguage Understanding and Generation

Huishuang Tian, Kexin Yang, Dayiheng Liu +1

Ancient Chinese is the essence of Chinese culture. There are several natural language processing tasks of ancient Chinese domain, such as ancient-modern Chinese translation, poem g…

cs.CL2020★ 14 cited

RikiNet: Reading Wikipedia Pages for Natural Question Answering

Dayiheng Liu, Yeyun Gong, Jie Fu +5

Reading long documents to answer open-domain questions remains challenging in natural language understanding. In this paper, we introduce a new model, called RikiNet, which reads W…

cs.CL2020★ 3 cited

Let's be Humorous: Knowledge Enhanced Humor Generation

Hang Zhang, Dayiheng Liu, Jiancheng Lv +1

The generation of humor is an under-explored and challenging problem. Previous works mainly utilize templates or replace phrases to generate humor. However, few works focus on free…

cs.CL2020★ 4 cited

Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation

Dayiheng Liu, Yeyun Gong, Jie Fu +6

News headline generation aims to produce a short sentence to attract readers to read the news. One news article often contains multiple keyphrases that are of interest to different…

cs.CL2020★ 2 cited

Generating Chinese Poetry from Images via Concrete and Abstract Information

Yusen Liu, Dayiheng Liu, Jiancheng Lv +1

In recent years, the automatic generation of classical Chinese poetry has made great progress. Besides focusing on improving the quality of the generated poetry, there is a new top…

cs.CL2019★ 6 cited

Deep Poetry: A Chinese Classical Poetry Generation System

Yusen Liu, Dayiheng Liu, Jiancheng Lv

In this work, we demonstrate a Chinese classical poetry generation system called Deep Poetry. Existing systems for Chinese classical poetry generation are mostly template-based and…

cs.CL2019★ 7 cited

TIGS: An Inference Algorithm for Text Infilling with Gradient Search

Dayiheng Liu, Jie Fu, Pengfei Liu +1

Text infilling is defined as a task for filling in the missing part of a sentence or paragraph, which is suitable for many real-world natural language generation scenarios. However…

cs.CL2019★ 5 cited

mu-Forcing: Training Variational Recurrent Autoencoders for Text Generation

Dayiheng Liu, Xu Yang, Feng He +2

It has been previously observed that training Variational Recurrent Autoencoders (VRAE) for text generation suffers from serious uninformative latent variables problem. The model w…

cs.CL2019★ 4 cited

Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning

Dayiheng Liu, Jie Fu, Yidan Zhang +2

Typical methods for unsupervised text style transfer often rely on two key ingredients: 1) seeking the explicit disentanglement of the content and the attributes, and 2) troublesom…

cs.CL2018★ 17 cited

Ancient-Modern Chinese Translation with a Large Training Dataset

Dayiheng Liu, Jiancheng Lv, Kexin Yang +1

Ancient Chinese brings the wisdom and spirit culture of the Chinese nation. Automatic translation from ancient Chinese to modern Chinese helps to inherit and carry forward the quin…

cs.CL2018★ 31 cited

A Multi-Modal Chinese Poetry Generation Model

Dayiheng Liu, Quan Guo, Wubo Li +1

Recent studies in sequence-to-sequence learning demonstrate that RNN encoder-decoder structure can successfully generate Chinese poetry. However, existing methods can only generate…

cs.CL2018★ 22 cited

BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation

Dayiheng Liu, Jie Fu, Qian Qu +1

Incorporating prior knowledge like lexical constraints into the model's output to generate meaningful and coherent sentences has many applications in dialogue system, machine trans…